Marriott Marquis Houston
Houston, Texas, United States
15 - 20 February 2020
Conference 11314
Computer-Aided Diagnosis
Sunday - Wednesday 16 - 19 February 2020
Important
Dates
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Abstract Due:
7 August 2019

Author Notification:
14 October 2019

Manuscript Due Date:
22 January 2020

Conference
Committee
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Conference Chairs
Program Committee
Program Committee continued...
Sunday 16 February Show All Abstracts
Session 1:
Mammography
Sunday 16 February 2020
8:00 AM - 9:40 AM
Location: Salon B
Session Chairs:
Susan M. Astley, The Univ. of Manchester (United Kingdom) ;
Horst K. Hahn, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
A hypersensitive breast cancer detector
Paper 11314-1
Author(s): Stefano Pedemonte, Whiterabbit.ai (United States)
Show Abstract
Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index
Paper 11314-2
Author(s): Yifan Peng, Tsinghua Univ. (China), Duke Univ. (United States); Rui Hou, Yinhao Ren, Lars Grimm, Jeffrey Marks, Shelley Hwang, Joseph Lo, Duke Univ. (United States)
Show Abstract
Performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution shift
Paper 11314-3
Author(s): Alexej Gossmann, Kenny H. Cha, U.S. Food and Drug Administration (United States); Xudong Sun, Ludwig-Maximilians-Univ. München (Germany)
Show Abstract
A multi-task deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in situ and segmenting microcalcifications in mammography
Paper 11314-4
Author(s): Rui Hou, Duke Univ. (United States); Maciej A. Mazurowski, Lars J. Grimm, Jeffrey R. Marks, Lorraine M. King, Duke Univ. School of Medicine (United States); Carlo C. Maley, Arizona State Univ. (United States); Shelley Hwang, Joseph Y. Lo, Duke Univ. School of Medicine (United States)
Show Abstract
Adaptation of a deep learning malignancy model from full-field digital mammography to digital breast tomosynthesis
Paper 11314-5
Author(s): Sadanand Singh, Thomas P. Matthews, Meet Shah, Brent Mombourquette, Aaron Long, Ranya Almohsen, Stefano Pedemonte, Jason Su, Whiterabbit.ai (United States)
Show Abstract
Session 2:
Chest I
Sunday 16 February 2020
10:10 AM - 12:10 PM
Location: Salon B
Session Chairs:
Matthew S. Brown, Univ. of California, Los Angeles (United States) ;
Samuel G. Armato, The Univ. of Chicago (United States)
Fast few-shot transfer learning for disease identification from chest x-ray images using autoencoder ensemble
Paper 11314-6
Author(s): Angshuman Paul, National Institutes of Health (United States); Yu-Xing Tang, Ronald M. Summers, National Institutes of Health (United States)
Show Abstract
Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features
Paper 11314-7
Author(s): Anindo Saha, Univ. de Girona (Spain); Fakrul I. Tushar, Carl E. Ravin Advanced Imaging Labs. (United States); Khrystyna Faryna, Univ. de Girona (Spain); Rui Hou, Carl E. Ravin Advanced Imaging Labs. (United States); Geoffrey D. Rubin, Duke Univ. School of Medicine (United States); Joseph Y. Lo, Carl E. Ravin Advanced Imaging Labs. (United States)
Show Abstract
Bone suppression on chest radiographs with adversarial learning
Paper 11314-8
Author(s): Jia Liang, National Institutes of Health Clinical Ctr. (United States), The Univ. of Tennessee Knoxville (United States); Yuxing Tang, Youbao Tang, National Institutes of Health Clinical Ctr. (United States); Jing Xiao, Ping An Technology Co., Ltd. (China); Ronald M. Summers, National Institutes of Health Clinical Ctr. (United States)
Show Abstract
Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT
Paper 11314-9
Author(s): Jordan Fuhrman, The Univ. of Chicago (United States); Rowena Yip, Claudia Henschke, David Yankelevitz, Icahn School of Medicine at Mount Sinai (United States); Maryellen Giger, The Univ. of Chicago (United States)
Show Abstract
Comparison of CNN architectures and training strategies for quantitative analysis of idiopathic interstitial pneumonia
Paper 11314-10
Author(s): Simon Rennotte, Télécom SudParis (France); Pierre-Yves Brillet, Avicenne Hospital (France); Catalin Fetita, Télécom SudParis (France)
Show Abstract
Deep learning for pneumothorax detection and localization using networks fine-tuned with multiple institutional datasets
Paper 11314-11
Author(s): Jennie Crosby, Thomas Rhines, Feng Li, Heber MacMahon, Maryellen Giger, The Univ. of Chicago (United States)
Show Abstract
Sunday/Monday Poster Viewing
Sunday 16 February 2020
12:00 PM - 9:00 PM
Location: Salon D/E

Posters will be on display Sunday and Monday with extended viewing until 9:00 pm on Sunday. The poster session with authors in attendance will be Monday evening from 5:30 to 7:00 pm. Award winners will be identified with ribbons during the reception. Award announcement times are listed in the conference schedule.
Lunch Break 12:10 PM - 1:20 PM
Session 3:
Neuro I
Sunday 16 February 2020
1:20 PM - 3:00 PM
Location: Salon B
Session Chairs:
Letícia Rittner, Univ. Estadual de Campinas (Brazil) ;
Khan M. Iftekharuddin, Old Dominion Univ. (United States)
Combining symmetric and standard deep convolutional representations for detecting acute hemorrhagic stroke
Paper 11314-12
Author(s): Arko Barman, UT Health Science Ctr. at Houston (United States); Victor Lopez-Rivera, Songmi Lee, Farhaan Vahidy, James Z. Fan, Sean I. Savitz, Sunil A. Sheth, Luca Giancardo, The Univ. of Texas Health Science Ctr. at Houston (United States)
Show Abstract
Generative synthetic adversarial network for internal bias correction and handling class imbalanced problem in multi-class medical image classification
Paper 11314-13
Author(s): Mina Rezaei, Janne Nappi, Massachusetts General Hospital, Harvard Medical School (United States); Konstantin Harmuth, Hasso-Plattner-Institut (Germany); Hiroyuki Yoshida, Massachusetts General Hospital, Harvard Medical School (United States); Christoph Meinel, Hasso-Plattner-Institut (Germany)
Show Abstract
Automatic detection of contrast enhancement in T1-weighted brain MRI of human adults
Paper 11314-14
Author(s): Mikhail Milchenko, Pamela LaMontagne, Daniel Marcus, Washington Univ. School of Medicine St. Louis (United States)
Show Abstract
A hyperacute stroke segmentation method using 3D U-Net integrated with physicians’ knowledge for NCCT
Paper 11314-15
Author(s): Takuya Fuchigami, Sadato Akahori, FUJIFILM Corp. (Japan); Takayuki Okatani, Graduate School of Information Sciences, Tohoku Univ. (Japan), RIKEN Ctr. for Advanced Intelligence Project (Japan); Yuanzhong Li, FUJIFILM Corp. (Japan)
Show Abstract
Deep learning with context encoding for semantic brain tumor segmentation and patient survival prediction
Paper 11314-16
Author(s): Khan M. Iftekharuddin, Monibor MD Rahman, Linmin Pei, Lasitha Vidyaratne, Old Dominion Univ. (United States)
Show Abstract
Session 4:
Abdomen
Sunday 16 February 2020
3:30 PM - 5:30 PM
Location: Salon B
Session Chairs:
Kenji Suzuki, Illinois Institute of Technology (United States) ;
Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Organ segmentation from full-size CT images using memory-efficient FCN
Paper 11314-17
Author(s): Chenglong Wang, Masahiro Oda, Kensaku Mori, Nagoya Univ. (Japan)
Show Abstract
A radiomics-based method for Cytokeratin 19 status prediction of Hepatocellular Carcinoma with Gadoxetic Acid–enhanced MRI
Paper 11314-18
Author(s): Dongsheng Gu, Institute of Automation (China), Univ. of Chinese Academy of Sciences (China); Wentao Wang, Zhongshan Hospital, Fudan Univ. (China); Jingwei Wei, Institute of Automation (China), Univ. of Chinese Academy of Sciences (China); Ying Ding, Li Yang, Kai Zhu, Zhongshan Hospital, Fudan Univ. (China); Rongkui Luo, Zhongshan Hospital, Fudan Univ. (China); Shengxiang Rao, Mengsu Zeng, Zhongshan Hospital, Fudan Univ. (China); Jie Tian, Institute of Automation (China), Beijing Advanced Innovation Ctr. for Big Data-Based Precision Medicine (China), Engineering Research Ctr. of Molecular and Neuro Imaging, Xidian Univ. (China)
Show Abstract
Multilevel UNet for pancreas segmentation from non-contrast CT scans through domain adaptation
Paper 11314-19
Author(s): Sai Aditya Sriram, National Institutes of Health Clinical Ctr. (United States), Rice Univ. (United States); Angshuman Paul, Yingying Zhu, Veit Sandfort, National Institutes of Health Clinical Ctr. (United States); Perry J. Pickhardt, Univ. of Wisconsin School of Medicine and Public Health (United States); Ronald Summers, National Institutes of Health Clinical Ctr. (United States)
Show Abstract
Robust hepatic vessels segmentation model based on noisy dataset
Paper 11314-20
Author(s): Li Liu, Jiang Tian, Cheng Zhong, Zhongchao Shi, Feiyu Xu, Lenovo (China)
Show Abstract
A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer
Paper 11314-21
Author(s): Amogh Hiremath, Rakesh Shiradkar, Case Western Reserve Univ. (United States); Harri Merisaari, Univ. of Turku (Finland), Case Western Reserve Univ. (United States); Nathaniel Braman, Prateek Prasanna, Case Western Reserve Univ. (United States); Otta Ettala, Univ. of Turku (Finland), Turku Univ. Hospital (Finland); Pekka Taimen, Institute of Biomedicine, Univ. of Turku (Finland), Turku Univ. Hospital (Finland); Hannu J. Aronen, Medical Imaging Ctr. of Southwest Finland, Turku Univ. Hospital (Finland); Peter J. Bostrom, Univ. of Turku (Finland), Turku Univ. Hospital (Finland); Ivan Jambor, Univ. of Turku (Finland); Andrei Purysko, Cleveland Clinic (United States); Anant Madabhushi, Case Western Reserve Univ. (United States)
Show Abstract
Integration of optical and virtual colonoscopy images for enhanced classification of colorectal polyps
Paper 11314-22
Author(s): Marc J. Pomeroy, Stony Brook Univ. (United States); Yi Wang, Tianjin Univ. (China); Anushka Banerjee, Almas Abbasi, Matthew A. Barish, Edward Sun, Juan Carlos Bucobo, Stony Brook Univ. (United States); Perry J. Pickhardt, Univ. of Wisconsin-Madison (United States); Zhengrong Liang, Stony Brook Univ. (United States)
Show Abstract
Session WK3:
Workshop: Live Demonstrations
Sunday 16 February 2020
5:45 PM - 7:45 PM
Location: Salon B

Workshop Chairs:
Dr. Lubomir Hadjiiski, Univ. of Michigan Health System (United States)
Dr. Karen Drukker, Univ. of Chicago (United States)

Panelists/Speaker(s):
To Be Determined

CALL FOR PARTICIPATION


The goal of this workshop is to provide a forum for systems and algorithms developers to show off their creations. The intent is for the audience to be inspired to conduct derivative research, for the demonstrators to receive feedback and find new collaborators, and for all to learn about the rapidly evolving field of medical imaging.

The Live Demonstration Workshop invites participation from all of the conferences that comprise the SPIE Medical Imaging symposium. We encourage the CAD, Digital Pathology, Image Processing, Imaging Informatics, Image Perception, Physics, and all other conferences to participate.

This workshop features interactive demonstrations that are complementary to the topics of SPIE Medical Imaging. Workshop demonstrations include samples, systems, and software demonstrations that depict the implementation, operation, and utility of cutting-edge as well as mature research. Having an accepted SPIE Medical Imaging paper is not required for giving a Live Demonstration; however, authors of SPIE Medical Imaging papers are encouraged to submit demonstrations that are complementary to their oral and poster presentations.

The session will include a Certificate of Merit Award presented to one demonstration considered to be of exceptional interest. We invite all workshop visitors to vote for three of their favorite demonstrations, with the final winner chosen from the top scorers by a group of appointed judges.

IMPORTANT DATES

  • January 17, 2020: Deadline for submission
  • January 24, 2020: Notification of acceptance
  • January 31, 2020: Deadline for two-slide summary


JOIN THE WORKSHOP


If you would like to demonstrate at the SPIE Medical Imaging Live Demonstrations Workshop, please send an e-mail with the subject "SPIE live demonstrations workshop" by the submission deadline to Lubomir Hadjiiski and Karen Drukker:
In the e-mail, supply the following information:
  • Title of the demo
  • Names and affiliations (name of institute, city, country) of the demonstrators
  • Short description of the demo, one paragraph minimum. Make sure it clearly describes the technology and application area of the demo. You may cite or include a paper describing the demo.
  • Optionally, describe the public data used in the development or evaluation of the system. Include a link to the data or to a page that describes how to access that data.
  • Optionally, include a link to a video showing the system in action.


NOTES

Please note the following rules and requirements:
  • The accepted demonstrations will be listed online in the workshop program.
  • If there are more proposals than presentation slots in the workshop, the organizers will accept teams for demonstrations based on the quality of the provided description, while also striving to select a representative mix of applications.
  • Each team is responsible for bringing their own equipment. The organization will provide a table and power supply for each demonstration. Demos should be done on a single laptop. If the demo requires an external monitor this is allowed, but there should be no more than one monitor of 25″ maximum size.
  • Participation in the workshop is free of charge, but all demonstrators (those present during the workshop) must be registered to attend the SPIE Medical Imaging Conference.
  • Teams from academia (universities, university medical centers, research organizations), and from industry are invited to participate in this year’s workshop. Demonstrations from industry should be scientific and not commercial in nature; demonstration of research prototypes is highly encouraged.
  • All participating teams will need to provide one or two slides describing their system shortly before the conference from which the opening presentation will be compiled (two-slide summary).
  • After you submit a description, you will receive a confirmation by e-mail. Notification of acceptance or rejection will follow on the date given above.
Monday 17 February Show All Abstracts
Session 5:
Musculoskeletal
Monday 17 February 2020
8:00 AM - 9:40 AM
Location: Salon B
Session Chairs:
Zhengrong Jerome Liang, Stony Brook Univ. (United States) ;
Axel Wismüller, Univ. of Rochester Medical Ctr. (United States)
Accurately identifying vertebral levels in large datasets
Paper 11314-23
Author(s): Daniel Elton, Veit Sandfort, National Institutes of Health Clinical Ctr. (United States); Perry J. Pickhardt, Univ. of Wisconsin-Madison (United States); Ronald M. Summers, National Institutes of Health Clinical Ctr. (United States)
Show Abstract
Semi-supervised learning for predicting total knee replacement with unsupervised data augmentation
Paper 11314-24
Author(s): Jimin Tan, Bofei Zhang, Kyunghyun Cho, New York Univ. (United States); Gregory Chang, Cem M. Deniz, NYU Langone Health (United States)
Show Abstract
Deciphering tissue relaxation parameters from a single MR image using deep learning
Paper 11314-25
Author(s): Yan Wu, Stanford Univ. (United States); Yajun Ma, Jiang Du, Univ. of California, San Diego (United States); Dante Capaldi, Lei Xing, Stanford Univ. (United States)
Show Abstract
Automatic Kellgren-Lawrence grade estimation driven deep learning algorithms
Paper 11314-26
Author(s): Nianyi Li, Albert Swiecicki, Nicholas Said, Jonathan O'Donnell, Maciej A. Mazurowski, Duke Univ. (United States)
Show Abstract
Computer-aided detection of focal bone metastases from whole-body multi-modal MRI
Paper 11314-27
Author(s): Jakub Ceranka, Vrije Univ. Brussel (Belgium); Frederic Lecouvet, Cliniques Univ. Saint-Luc (Belgium); Johan De Mey, Univ. Ziekenhuis Brussel (Belgium); Jef Vandemeulebroucke, Vrije Univ. Brussel (Belgium)
Show Abstract
Session 6:
Radiomics
Monday 17 February 2020
10:10 AM - 12:10 PM
Location: Salon B
Session Chairs:
Kensaku Mori, Nagoya Univ. (Japan) ;
Heang-Ping Chan, Michigan Medicine (United States)
U-radiomics for predicting survival of patients with idiopathic pulmonary fibrosis
Paper 11314-28
Author(s): Tomoki Uemura, Chinatsu Watari, Janne J. Näppi, Hironaka Toru, Massachusetts General Hospital, Harvard Medical School (United States); Hyoungseop Kim, Kyushu Institute of Technology (Japan); Hiroyuki Yoshida, Massachusetts General Hospital, Harvard Medical School (United States)
Show Abstract
Dependence of radiomics features on CT image acquisition and reconstruction parameters using a cadaveric liver
Paper 11314-29
Author(s): Joseph J. Foy, Inna Gertsenshteyn, Hania A. Al-Hallaq, Samuel G. Armato, The Univ. of Chicago (United States); William Sensakovic, Mayo Clinic (United States)
Show Abstract
Multi-site evaluation of stable radiomic features for more accurately evaluating response to chemoradiation in rectal cancers via MRI
Paper 11314-30
Author(s): Amrish Selvam, Jacob Antunes, Kaustav Bera, Case Western Reserve Univ. (United States); Asya Ofshteyn, Univ. Hospitals of Cleveland (United States); Justin T. Brady, Univ. Hospitals of Cleveland (United States); Kenneth Friedman, Sharon Stein, Rajmohan Paspulati, Univ. Hospitals of Cleveland (United States); Andrei Purysko, Matthew Kalady, The Cleveland Clinic Foundation (United States); Anant Madabhushi, Satish E. Viswanath, Case Western Reserve Univ. (United States)
Show Abstract
Robust radiomic feature selection in digital mammography: understanding the effect of imaging acquisition physics using phantom and clinical data analysis
Paper 11314-31
Author(s): Raymond J. Acciavatti, Penn Medicine (United States); Eric A. Cohen, Omid Haji Maghsoudi, Penn Medicine (United States); Aimilia Gastounioti, Penn Medicine (United States); Lauren Pantalone, Penn Medicine (United States); Meng-Kang Hsieh, Emily F. Conant, Penn Medicine (United States); Christopher G. Scott, Stacey J. Winham, Mayo Clinic (United States); Karla Kerlikowske, Univ. of California, San Francisco (United States); Celine Vachon, Mayo Clinic (United States); Andrew D. A. Maidment, Despina Kontos, Penn Medicine (United States)
Show Abstract
Improvement of classification performance using harmonization across field strength of radiomic features extracted from DCE-MR images of the breast
Paper 11314-33
Author(s): Heather M. Whitney, Wheaton College (United States), The Univ. of Chicago (United States); Maryellen L. Giger, The Univ. of Chicago (United States)
Show Abstract
Machine learning-powered prediction of recurrence in patients with non-small cell lung cancer using quantitative clinical and radiomic biomarkers
Paper 11314-32
Author(s): Sehwa Moon, Dahim Choi, Ji-Yeon Lee, Myoung-Hee Kim, Ewha Womans Univ. (Korea, Republic of); Helen Hong, Seoul Women's Univ. (Korea, Republic of); Bong-Seog Kim, Veterans Health Service Medical Ctr. (Korea, Republic of); Jang-Hwan Choi, Ewha Womans Univ. (Korea, Republic of)
Show Abstract
Lunch Break 12:10 PM - 1:20 PM
Session 7:
Breast MRI, Skin
Monday 17 February 2020
1:20 PM - 3:40 PM
Location: Salon B
Session Chairs:
Maryellen L. Giger, The Univ. of Chicago (United States) ;
Thomas Martin Deserno, Technische Univ. Braunschweig (Germany)
Explainable AI for medical imaging: deep-learning CNN ensemble for classification of estrogen receptor status from breast MRI
Paper 11314-52
Author(s): Zachary Papanastasopoulos, Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, Chintana Paramagul, Mark A. Helvie, Colleen H. Neal, Univ. of Michigan (United States)
Show Abstract
Long short-term memory networks predict breast cancer recurrence in analysis of consecutive MRIs acquired during the course of neoadjuvant chemotherapy
Paper 11314-34
Author(s): Karen Drukker, Univ. of Chicago Medical Ctr. (United States); Alexandra Edwards, John Papaioannou, Maryellen Giger, Univ. of Chicago Medical Ctr. (United States)
Show Abstract
Using ResNet feature extraction in computer-aided diagnosis of breast cancer on 927 lesions imaged with multiparametric MRI
Paper 11314-35
Author(s): Qiyuan Hu, The Univ. of Chicago (United States); Heather M. Whitney, Wheaton College (United States), The Univ. of Chicago (United States); Maryellen L. Giger, The Univ. of Chicago (United States)
Show Abstract
Interpretable deep learning regression for breast density estimation on MRI
Paper 11314-69
Author(s): Bas H.M. van der Velden, Max A. A. Ragusi, Markus H. A. Janse, Univ. Medical Ctr. Utrecht (Netherlands); Claudette E. Loo, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital (Netherlands); Kenneth G. A. Gilhuijs, Univ. Medical Ctr. Utrecht (Netherlands)
Show Abstract
MRI image harmonization using cycle-consistent generative adversarial network
Paper 11314-36
Author(s): Gourav Modanwal, Adithya Vellal, Mateusz Buda, Maciej A. Mazurowski, Duke Univ. (United States)
Show Abstract
Melanoma detection with electrical impedance spectroscopy and dermoscopy using joint deep learning models
Paper 11314-37
Author(s): Nils Gessert, Marcel Bengs, Alexander Schlaefer, Technische Univ. Hamburg-Harburg (Germany)
Show Abstract
A multidimensional scaling and sample clustering to obtain a representative subset of training data for transfer learning based Rosacea lesion identification
Paper 11314-38
Author(s): Hamidullah Binol, M. Khalid Khan Niazi, Wake Forest Ctr. for Biomedical Informatics (United States); Benjamin Kaffenberger, The Ohio State Univ. (United States); Metin N. Gurcan, Wake Forest Ctr. for Biomedical Informatics (United States)
Show Abstract
Session Plen:
Awards and Plenary Session
Monday 17 February 2020
4:00 PM - 5:15 PM
Location: Salon F

Session Chairs: Metin N. Gurcan, Wake Forest Baptist Medical Ctr. (United States) and Georgia D. Tourassi, Oak Ridge National Lab. (United States)

4:00 PM - 4:30 PM: Award presentations
Are today's Mixed Reality experience pillars and hardware architectures well aligned with the specific needs of medical imaging and surgical guidance? (Plenary Presentation)
Paper 11313-8
Author(s): Bernard C. Kress, Microsoft Corp. (United States)
Show Abstract
Session PSMon:
Monday Poster Session
Monday 17 February 2020
5:30 PM - 7:00 PM
Location: Salon D/E
DCGANs for realistic breast mass augmentation in x-ray mammography
Paper 11314-68
Author(s): Basel Alyafi, Computer Vision and Robotics Institute, Univ. de Girona (Spain); Oliver Diaz, Computer Vision and Robotics Institute, Univ. de Girona (Spain), Ctr. for Digital Medical Imaging, Parc Taulí Hospital Univ. (Spain); Robert Martí , Computer Vision and Robotics Institute, Univ. de Girona (Spain)
Show Abstract
Case-based repeatability of machine learning classification performance on breast MRI
Paper 11314-70
Author(s): Michael Vieceli, Amy Van Dusen, Wheaton College (United States); Karen Drukker, Hiroyuki Abe, Maryellen L. Giger, The Univ. of Chicago (United States); Heather M. Whitney, Wheaton College (United States), The Univ. of Chicago (United States)
Show Abstract
Genetic algorithm for machine learning architecture selection for breast MRI classification
Paper 11314-71
Author(s): Peter Carras, Michigan State Univ. (United States); Carina Pereira, Debosmita Biswas, Christoph Lee, Savannah Partridge, Univ. of Washington (United States); Adam Alessio, Michigan State Univ. (United States)
Show Abstract
Automated breast cancer risk estimation on routine CT thorax scans by deep learning segmentation
Paper 11314-72
Author(s): Stijn De Buck, UZ Leuven (Belgium); Jeroen Bertels, KU Leuven (Belgium); Chelsey Vanbilsen, Tanguy Dewaele, ZOL (Belgium); Chantal Van Ongeval, Hilde Bosmans, UZ Leuven (Belgium); Jan Vandevenne, ZOL (Belgium); Paul Suetens, KU Leuven (Belgium)
Show Abstract
Automatic detection and classification of abnormal tissues on digital mammograms based on a bag-of-visual-words approach
Paper 11314-73
Author(s): Laura A. Zanella-Calzada, Univ. Autónoma de Zacatecas (Mexico); Erick Rodríguez-Esparza, Universidad de Guadalajara (Mexico); Diego Oliva, Marco Pérez-Cisneros, Univ. de Guadalajara (Mexico)
Show Abstract
A hybrid approach for mammary gland segmentation on CT images by embedding visual explanations from a deep learning classifier into a Bayesian inference
Paper 11314-74
Author(s): Xiangrong Zhou, Seiya Yamagishi, Takeshi Hara, Hiroshi Fujita, Gifu Univ. (Japan)
Show Abstract
Generating high resolution digital mammogram from digitized film mammogram with conditional generative adversarial network
Paper 11314-75
Author(s): Yuanpin Zhou, Sun Yat-Sen Univ. (China), Univ. of Michigan (United States); Jun Wei, Mark A. Helvie, Heang-ping Chan, Chuan Zhou, Hadjiiski Lubomir, Univ. of Michigan (United States); Yao Lu, Sun Yat-Sen Univ. (China)
Show Abstract
Visualization of multiview mammographic mass detection system using GRAD-CAM
Paper 11314-76
Author(s): Yinhao Ren, Duke Univ. (United States)
Show Abstract
Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images
Paper 11314-77
Author(s): Albert Swiecicki, Mateusz Buda, Ashirbani Saha, Nianyi Li, Sujata V. Ghate, Ruth Walsh, Maciej A. Mazurowski, Duke Univ. (United States)
Show Abstract
3D U-Net for segmentation of pulmonary nodules in volumetric CT scans from multi-annotator truth estimation
Paper 11314-78
Author(s): William Funke, Benjamin Veasey, Jacek Zurada, Hichem Frigui, Amir Amini, Univ. of Louisville (United States)
Show Abstract
Semi-supervised convolutional neural network for automated segmentation of diffuse lung disease patterns
Paper 11314-79
Author(s): Yuki Suzuki, Kazuki Yamagata, Shoji Kido, Noriyuki Tomiyama, Osaka Univ. (Japan)
Show Abstract
False positive reduction of vasculature for pulmonary nodule detection
Paper 11314-80
Author(s): Colin B. Hansen, Vanderbilt Univ. (United States); Yiyuan Zhao, Halid Yerebakan, Luca Bogoni, Anna Jerebko, Siemens Medical Solutions (United States)
Show Abstract
Multi-task learning for mortality prediction in LDCT images
Paper 11314-81
Author(s): Hengtao Guo, Rensselaer Polytechnic Institute (United States); Melanie Kruger, Shenendehowa High School (United States); Ge Wang, Rensselaer Polytechnic Institute (United States); Mannudeep K. Kalra, Massachusetts General Hospital (United States); Pingkun Yan, Rensselaer Polytechnic Institute (United States)
Show Abstract
Association analysis of SNPs with CT image-based phenotype of emphysema progression in heavy smokers
Paper 11314-82
Author(s): Hidenobu Suzuki, Mikio Matsuhiro, Yoshiki Kawata, Noboru Niki, Tokushima Univ. (Japan); Issei Imoto, Aichi Cancer Ctr. Research Institute (Japan); Yasutaka Nakano, Shiga Univ. of Medical Science (Japan); Masahiko Kusumoto, National Cancer Ctr. Hospital East (Japan); Masahiro Kaneko, Tokyo Health Service Association (Japan)
Show Abstract
Active semi-supervised expectation-maximization learning for lung cancer detection from Computerized Tomography (CT) images with minimally label training data
Paper 11314-83
Author(s): Phuong Nguyen, David Chapman, Sumeet Menon, Univ. of Maryland, Baltimore County (United States); Michael Morris, Mercy Medical Ctr. (United States), Univ. of Maryland, Baltimore County (United States); Yelena Yesha, Univ. of Maryland, Baltimore County (United States)
Show Abstract
Artificially augmenting data or adding more samples? A study on a 3D CNN for lung nodule classification
Paper 11314-84
Author(s): Panagiotis Gonidakis, Bart Jansen, Jef Vandemeulebroucke, Vrije Univ. Brussel (Belgium), imec (Belgium)
Show Abstract
Assessment of CT image reconstruction parameters on radiomic features in a lung cancer screening cohort: the PROSPR study
Paper 11314-85
Author(s): Babak Haghighi, Peter B. Noël, Eric A. Cohen, Lauren Pantalone, Anil Vachani, Katharine Rendle, Eduardo Mortani Barbosa , Despina Kontos, Univ. of Pennsylvania (United States)
Show Abstract
Deep learning-based automatic reporting system for lung cancer screening program
Paper 11314-86
Author(s): Hyunho Park, Seungho Lee, Gwangbeen Park, Minsuk Park, Jin-Kyeong Sung, Sangkeun Kim, Kyu-Hwan Jung, VUNO Inc. (Korea, Republic of)
Show Abstract
Deep learning methods for segmentation of lines in pediatric chest radiographs
Paper 11314-87
Author(s): Ryan P. Sullivan, Purdue Univ. (United States), Michigan State Univ. (United States); Greg Holste, Michigan State Univ. (United States), Kenyon College (United States); Jonathan Burkow, Adam Alessio, Michigan State Univ. (United States)
Show Abstract
Differential diagnosis of pulmonary nodules using 3D CT image
Paper 11314-88
Author(s): Takeru Kageyama, Noboru Niki, Yoshiki Kawata, Tokushima Univ. (Japan); Masahiko Kusumoto, Yoshiki Aokage, Genichirou Ishii, National Cancer Ctr. Hospital East (Japan); Hironobu Ohmatsu, Abashiri Prison (Japan); Takaaki Tsuchida, Yuji Matsumoto, National Cancer Ctr. Hospital East (Japan); Kenji Eguchi, Teikyo Univ. (Japan); Masahiro Kaneko, Tokyo Health Service Association (Japan)
Show Abstract
Radiomics-based texture analysis of Idiopathic Pulmonary Fibrosis for genetic and survival predictions
Paper 11314-89
Author(s): Jorie D. Budzikowski, Vanderbilt Univ. (United States); Ahmed A. Rashid, Rollins College (United States); Joseph J. Foy, Jonathan H. Chung, Samuel G. Armato, The Univ. of Chicago (United States)
Show Abstract
Lung tumor segmentation using coupling-net with shape-focused prior on chest CT images of non-small cell lung cancer patients
Paper 11314-90
Author(s): Sohyun Byun, Julip Jung, Helen Hong, Seoul Women's Univ. (Korea, Republic of); Hoonil Oh, Nowon Radiology Clinic (Korea, Republic of); Bong Seog Kim, Veterans Health Service Medical Ctr. (Korea, Republic of)
Show Abstract
Multi-modal component subspace similarity based multi-kernel SVM for multi-site classification
Paper 11314-139
Author(s): Shuang Gao, Institute of Automation (China); Vince D. Calhoun, Tri-institutional Ctr. for Translational Research in Neuroimaging and Data Science (United States); Jing Sui, Institute of Automation (China)
Show Abstract
Deep convolutional neural networks for molecular subtyping of gliomas using magnetic resonance imaging
Paper 11314-91
Author(s): Dong Wei, Tencent (China); Yiming Li, Yinyan Wang, Capital Medical Univ. (China); Tianyi Qian, Tencent (China), Sinovation Ventures (China); Yefeng Zheng, Tencent (China)
Show Abstract
Lesion conditional image generation for improved segmentation of intracranial hemorrhage from CT images
Paper 11314-92
Author(s): Manohar Karki, Junghwan Cho, Eunmi Lee, CAIDE Systems (United States)
Show Abstract
Feasibility of using recurrent neural networks to predict treatment outcome of intracranial aneurysms using angiographic parametric imaging
Paper 11314-93
Author(s): Mohammad Mahdi Shiraz Bhurwani, Kyle A. Williams, Univ. at Buffalo (United States), Canon Stroke and Vascular Research Ctr. (United States); Mohammad Waqas, Canon Stroke and Vascular Research Ctr. (United States), Univ. at Buffalo (United States); Ryan A. Rava, Alexander R. Podgorsak, Univ. at Buffalo (United States), Canon Stroke and Vascular Research Ctr. (United States); Kenneth V. Snyder, Canon Stroke and Vascular Research Ctr. (United States), University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine (United States); Elad I. Levy, Canon Stroke and Vascular Research Ctr. (United States), Univ. at Buffalo (United States); Jason M. Davies, Canon Stroke and Vascular Research Ctr. (United States), University at Buffalo Neurosurgery, University at Buffalo Jacobs School of Medicine (United States); Adnan H. Siddiqui, Canon Stroke and Vascular Research Ctr. (United States), Univ. at Buffalo (United States); Ciprian N. Ionita, Univ. at Buffalo (United States), Canon Stroke and Vascular Research Ctr. (United States)
Show Abstract
Retrospective study on computer-aided brain metastasis detection using 3D MR imaging
Paper 11314-94
Author(s): Eunji Kim, Gyeong Yun Yi, Ye Rang Park, Young Jae Kim, Kwang Gi Kim, Gachon Univ. (Korea, Republic of)
Show Abstract
Deep learning-based brain tumor bed segmentation for dynamic magnetic resonance perfusion imaging
Paper 11314-95
Author(s): Jiwoong J. Jeong, Yang Lei, Hyunsuk Shim, Hui Mao, Tian Liu, Hui-Kuo Shu, Xiaofeng Yang, Emory Univ. (United States)
Show Abstract
Prediction of MCI to AD risk of conversion survival models: qMRI vs CSF measures and cognitive assessments
Paper 11314-96
Author(s): Jorge Orozco-Sanchez, José Tamez-Peña, Tecnológico de Monterrey (Mexico)
Show Abstract
Diagnosis of Parkinson's Disease with a hybrid feature selection algorithm based on a discrete artificial bee colony
Paper 11314-97
Author(s): Haolun Li, Nanjing Univ. of Posts and Telecommunications (China); Rui Zong, Longsheng Pan, Xin Xu, Chinese PLA General Hospital (China); Qionghai Dai, Feng Xu, Tsinghua Univ. (China); Hao Gao, Nanjing Univ. of Posts and Telecommunications (China)
Show Abstract
Advanced magnetic resonance imaging based algorithm for local grading of glioma
Paper 11314-98
Author(s): Evan Gates, Jonathan Lin, Jeffrey S. Weinberg, Sujit S. Prabhu, The Univ. of Texas M. D. Anderson Cancer Ctr. (United States); Jackson Hamilton, The Univ. of Texas M. D. Anderson Cancer Ctr. (United States), Radiology Partners, Inc. (United States); John D. Hazle, Gregory N. Fuller, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States); Veera Baladandayuthapani, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States), Univ. of Michigan School of Public Health (United States); David Fuentes, Dawid Schellingerhout, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Show Abstract
AI-assisted in situ detection of human glioma infiltration using deep neural networks for optical coherence tomography
Paper 11314-99
Author(s): Ronald Juarez Chambi, Texas A&M Univ. (United States); Carmen Kut, Johns Hopkins Univ. (United States); Kaisorn L. Chaichana, Alfredo Quiñones-Hinojosa, Mayo Clinic (United States); Xingde Li, Johns Hopkins Univ. (United States); Javier Jo, The Univ. of Oklahoma (United States)
Show Abstract
A data-driven approach for stratifying psychotic and mood disorders subjects using structural MRI data
Paper 11314-100
Author(s): Hooman Rokham, Vince D. Calhoun, Ctr. for Translational Research in Neuroimaging and Data Science (United States)
Show Abstract
Automated localization and segmentation of major cerebral vasculature with aneurysms from 3D DSA using deep learning
Paper 11314-101
Author(s): Tatsat Rajendra Patel, Nikhil Paliwal, Canon Stroke and Vascular Research Ctr. (United States); Prakhar Jaiswal, Muhammad Waqas, Univ. at Buffalo (United States); Maxim Mokin, Univ. of South Florida (United States); Adnan H. Siddiqui, Rahul Rai, Univ. at Buffalo (United States); Hui Meng, Canon Stroke and Vascular Research Ctr. (United States)
Show Abstract
Automatic detection of brain metastases using 3D mask R-CNN for stereotactic radiosurgery
Paper 11314-102
Author(s): Yang Lei, Zhen Tian, Shannon Kahn, Walter J. Curran, Tian Liu, Xiaofeng Yang, Emory Univ. (United States)
Show Abstract
Automatic arteriovenous malformations segmentation on brain CT using combined region proposal network and V-Net
Paper 11314-103
Author(s): Yabo Fu, Yang Lei, Tonghe Wang, Xiaojun Jiang, Tian Liu, Walter J. Curran, Hui-Kuo Shu, Xiaofeng Yang, Emory Univ. (United States)
Show Abstract
Computer-assisted quantification of surgical outcome in infants with sagittal craniosynostosis in 3D head CT images using mean normal skull model
Paper 11314-104
Author(s): Min Jin Lee, Helen Hong, Seoul Women's Univ. (Korea, Republic of); Kyu Won Shim, Yonsei Univ. College of Medicine (Korea, Republic of)
Show Abstract
Using ``Lesion-habitat'' radiomics to distinguish radiation necrosis from tumor recurrence on post-treatment MRI in metastatic brain tumors
Paper 11314-105
Author(s): Ramon Correa, Jonathan Chen, Pallavi Tiwari, Case Western Reserve Univ. (United States); Jennifer Yu, Johnathan Zeng, Qiu Lei, Cleveland Clinic (United States)
Show Abstract
First steps into endoscopic video analysis for Barrett’s cancer detection: challenges and opportunities
Paper 11314-106
Author(s): Joost van der Putten, Technische Univ. Eindhoven (Netherlands); Jeroen de Groof, Amsterdam UMC (Netherlands); Fons van der Sommen, Technische Univ. Eindhoven (Netherlands); Maarten Struyvenberg, Amsterdam UMC (Netherlands); Svitlana Zinger, Technische Univ. Eindhoven (Netherlands); Wouter Curvers, Erik Schoon, Catharina Hospital (Netherlands); Jacques Bergman, Amsterdam UMC (Netherlands); Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)
Show Abstract
Deep learning applied to hyperspectral endoscopy for online spectral classification
Paper 11314-107
Author(s): Alexandru Grigoroiu, Univ. of Cambridge (United Kingdom), Cancer Research UK Cambridge Institute, Univ. of Cambridge (United Kingdom); Jonghee Yoon, Sarah E. Bohndiek, Univ. of Cambridge (United Kingdom), Cancer Research UK Cambridge Institute (United Kingdom)
Show Abstract
A novel multi-classifier system for triaging patients with suspected prostate cancer using 3D convolutional neural networks and volumetric biparametric MRI
Paper 11314-108
Author(s): Pritesh Mehta, Michela Antonelli, Hashim Ahmed, Mark Emberton, Shonit Punwani, Univ. College London (United Kingdom); Sebastien Ourselin, King's College London (United Kingdom)
Show Abstract
Visualizing intestines for diagnostic assistance of ileus based on intestinal region segmentation from 3D CT images
Paper 11314-109
Author(s): Hirohisa Oda, Kohei Nishio, Nagoya Univ. (Japan); Takayuki Kitasaka, Aichi Institute of Technology (Japan); Hizuru Amano, The Univ. of Tokyo (Japan), Nagoya Univ. (Japan); Aitaro Takimoto, Akinari Hinoki, Hiroo Uchida, Nagoya Univ. (Japan); Kojiro Suzuki, Aichi Medical Univ. (Japan); Hayato Itoh, Masahiro Oda, Nagoya Univ. (Japan); Kensaku Mori, Nagoya Univ. (Japan), National Institute of Informatics (Japan)
Show Abstract
Comparative performance of 3D-DenseNet, 3D-ResNet, and 3D-VGG models in polyp detection for CT colonography
Paper 11314-110
Author(s): Tomoki Uemura, Massachusetts General Hospital (United States), Kyushu Institute of Technology (Japan); Janne J. Näppi, Toru Hironaka, Massachusetts General Hospital (United States); Hyoungseop Kim, Kyushu Institute of Technology (Japan); Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Show Abstract
Machine learning methods to classify presence of intestine damage on Computed Tomography in patients with Crohn’s disease
Paper 11314-111
Author(s): Binu E. Enchakalody, Brianna Henderson, Stewart Wang, Grace L. Su, Mahmoud Al-Hawary, Ashish Wasnik, Ryan W. Stidham, Michigan Medicine (United States)
Show Abstract
The field effect in Barrett's Esophagus: a macroscopic view using white light endoscopy and deep learning
Paper 11314-112
Author(s): Levi Verhage, Joost van der Putten, Fons van der Sommen, Peter de With, Technische Univ. Eindhoven (Netherlands); Jeroen de Groof, Maarten Struyvenberg, Amsterdam UMC (Netherlands)
Show Abstract
Visualising decision-reasoning regions in computer-aided pathological pattern diagnosis of endoscytoscopic images based on CNN weights analysis
Paper 11314-113
Author(s): Hayato Itoh, Zhongyang Lu, Masahiro Oda, Nagoya Univ. (Japan); Yuichi Mori, Masashi Misawa, Shin-ei Kudo, Showa Univ. Northern Yokohama Hospital (Japan); Kensaku Mori, Nagoya Univ. (Japan)
Show Abstract
Computer-aided staging of gastric cancer using radiomics signature on computed tomography imaging
Paper 11314-114
Author(s): Lili Wang, Fujian Medical Univ. Union Hospital (China); Bin Zheng, The Univ. of Oklahoma (United States); Jie Wu, Shanghai Key Lab. of Magnetic Resonance, East China Normal Univ. (China); Guang Yang, Shanghai Key Lab. of Magnetic Resonance, East China Normal Univ. (China)
Show Abstract
Automatic polyp detection and localization during colonoscopy using convolutional neural networks
Paper 11314-115
Author(s): Diego Bravo, Josue Ruano, Martin Gomez, Eduardo Romero Castro, Univ. Nacional de Colombia Sede Bogotá (Colombia)
Show Abstract
Performance investigation of deep learning versus classifier for polyp differentiation via texture features
Paper 11314-116
Author(s): David Liang, David Wang, Alice Wei, Yeseul Choi, Shu Zhang, Marc J. Pomeroy, Stony Brook Univ. (United States); Perry J. Pickhardt, Univ. of Wisconsin-Madison (United States)
Show Abstract
Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography
Paper 11314-117
Author(s): Janne J. Näppi, Massachusetts General Hospital (United States), Harvard Medical School (United States); Tomoki Uemura, Massachusetts General Hospital (United States); Se Hyung Kim, Seoul National Univ. Hospital (Korea, Republic of), Seoul National Univ. College of Medicine (Korea, Republic of); Hyoungseop Kim, Kyushu Institute of Technology (Japan); Hiroyuki Yoshida, Massachusetts General Hospital (United States), Harvard Medical School (United States)
Show Abstract
A deep learning based integration of multiple texture patterns from intensity, gradient and curvature GLCMs in differentiating the malignant from benign polyps
Paper 11314-118
Author(s): Shu Zhang, Weiguo Cao, Marc Pomeroy, Yongfeng Gao, Stony Brook Univ. (United States); Jiaxing Tan, The City Univ. of New York (United States); Zhengrong Liang, Stony Brook Univ. (United States)
Show Abstract
Deformation robust texture features for polyp classification via CT colonography
Paper 11314-119
Author(s): Weiguo Cao, Marc J. Pomeroy, Shu Zhang, Stony Brook Univ. (United States); Perry J. Pickhardt, Univ. of Wisconsin-Madison (United States); Hongbing Lu, Fourth Military Medical Univ. (China); Zhengrong Liang, The State Univ. of New York at Stony Brook (United States)
Show Abstract
Evaluating texture-based prostate cancer classification on multi-parametric magnetic resonance imaging and prostate specific membrane antigen positron emission tomography
Paper 11314-120
Author(s): Ryan Alfano, Glenn Bauman, Western Univ. (Canada); Jonathan Thiessen, Western Univ. (Canada), Lawson Health Research Institute (Canada); Irina Rachinsky, William Pavlosky, John Butler, Lawson Health Research Institute (Canada); Mena Gaed, Madeleine Moussa, Jose Gomez-Lemus, Joseph Chin, Stephen Pautler, Western Univ. (Canada); Aaron Ward, Western Univ. (Canada), Lawson Health Research Institute (Canada)
Show Abstract
Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: preliminary results
Paper 11314-121
Author(s): Rakesh Shiradkar, Ruyuan Zuo, Case Western Reserve Univ. (United States); Amr Mahran, Univ. Hospitals of Cleveland (United States); Lee Ponsky, Case Western Reserve Univ. (United States); Sree Harsha Tirumani, Radiology (United States); Anant Madabhushi, Case Western Reserve Univ. (United States)
Show Abstract
Automatic liver segmentation in abdominal CT images using combined 2.5D and 3D segmentation networks with high-score shape prior for radiotherapy treatment planning
Paper 11314-122
Author(s): Julip Jung, Helen Hong, Seoul Women's Univ. (Korea, Republic of); Taesik Jeong, Yonsei Cancer Ctr. (Korea, Republic of); Jinsil Seong, Jin Sung Kim, Yonsei Univ. College of Medicine (Korea, Republic of)
Show Abstract
Prediction of prostate cancer aggressiveness using quantitative radiomic features using multi-parametric MRI
Paper 11314-123
Author(s): Julip Jung, Seoul Women's Univ. (Kosovo, Republic of); Helen Hong, Seoul Women's Univ. (Korea, Republic of); Young-Gi Kim, Sung Il Hwang, Hak Jong Lee, Seoul National Univ. Bundang Hospital (Korea, Republic of)
Show Abstract
Renal parenchyma segmentation in abdominal CT images based on deep convolutional neural networks with similar atlas selection and transformation
Paper 11314-124
Author(s): Hyeonjin Kim, Helen Hong, Seoul Women's Univ. (Korea, Republic of); Koon Ho Rha, Yonsei Univ. College of Medicine (Korea, Republic of)
Show Abstract
Bladder wall segmentation using U-net based deep learning
Paper 11314-125
Author(s): Michael I. Ivanitskiy, Lubomir Hadjiyski, Ravi K. Samala, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Alon Z. Weizer, Ajjai Alva, Jun Wei, Chuan Zhou, Univ. of Michigan (United States)
Show Abstract
Survival prediction of liver cancer patients from CT images using deep learning and radiomic feature-based regression
Paper 11314-126
Author(s): Hansang Lee, KAIST (Korea, Republic of); Helen Hong, Seoul Women's Univ. (Korea, Republic of); Jinsil Seong, Jin Sung Kim, Yonsei Univ. College of Medicine (Korea, Republic of); Junmo Kim, KAIST (Korea, Republic of)
Show Abstract
CNN-based detection of distal tibial fractures in radiographic images in the setting of open growth plates
Paper 11314-127
Author(s): Zbigniew Starosolski, J. Herman Kan, Ananth Annapragada, Texas Children's Hospital (United States)
Show Abstract
Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms
Paper 11314-128
Author(s): Albert Swiecicki, Nicholas Said, Jonathan O'Donnell, Mateusz Buda, Nianyi Li, William A. Jiranek, Maciej A. Mazurowski, Duke Univ. (United States)
Show Abstract
Detection of knee orientation for MR slice positioning using 3D U-Net
Paper 11314-129
Author(s): Chen Li, Dartmouth College (United States); Parmeet S. Bhatia, Yu Zhao, Siemens Healthineers (United States)
Show Abstract
Classification of lesion specific myocardial ischemia using cardiac computed tomography radiomics
Paper 11314-130
Author(s): Bang Jun Guo, Xiuxiu He, Tonghe Wang, Yang Lei, Tian Liu, Long Jiang Zhang, Xiaofeng Yang, Emory Univ. (United States)
Show Abstract
Siamese neural networks for the classification of high-dimensional radiomic features
Paper 11314-131
Author(s): Abhishaike Mahajan, James D. Dormer, The Univ. of Texas at Dallas (United States); Qinmei Li, The Univ. of Texas at Dallas (United States), Second Affiliated Hospital of Guangzhou Medical University (China); Deji Chen, Zhenfeng Zhang, The Second Affiliated Hospital of Guangzhou Medical Univ. (China); Baowei Fei, The Univ. of Texas at Dallas (United States)
Show Abstract
Survey of image denoising methods for medical image classification
Paper 11314-132
Author(s): Peter Michael, Univ. of Washington (United States); Hong-Jun Yoon, Oak Ridge National Lab. (United States)
Show Abstract
Automatic cancer detection and localization using multispectral photoacoustic imaging
Paper 11314-133
Author(s): Kamal Jnawali, Rochester Institute of Technology (United States); Bhargava Chinni, Vikram Dogra, Univ. of Rochester (United States); Navalgund Rao, Rochester Institute of Technology (United States)
Show Abstract
Evaluating several ways to combine handcrafted features-based system with a deep learning system using the LUNA16 Challenge framework
Paper 11314-134
Author(s): Alexander Sóñora-Mengan, Univ. de Oriente (Cuba); Panagiotis Gonidakis, Bart Jansen, Vrije Univ. Brussel (Belgium); Juan Garcı́a-Naranjo, Univ. de Oriente (Cuba); Jef Vandemeulebroucke, Vrije Univ. Brussel (Belgium)
Show Abstract
A post-acquisition standardization method for positron emission tomography images
Paper 11314-135
Author(s): Aliasghar Mortazi, Jayaram K. Udupa, Yubing Tong, Drew A. Torigian, Univ. of Pennsylvania (United States)
Show Abstract
Usefulness of fine-tuning for deep learning based multi-organ regions segmentation method from non-contrast CT volumes using small training dataset
Paper 11314-136
Author(s): Yuichiro Hayashi, Chen Shen, Holger R. Roth, Masahiro Oda, Nagoya Univ. (Japan); Kazunari Misawa, Aichi Cancer Ctr. Hospital (Japan); Masahiro Jinzaki, Masahiro Hashimoto, Keio Univ. (Japan); Kanako K. Kumamaru, Shigeki Aoki, Juntendo Univ. (Japan); Kensaku Mori, Nagoya Univ. (Japan)
Show Abstract
Synthesize CT from paired MRI of the same patient with patch-based generative adversarial network
Paper 11314-137
Author(s): Yan Li, Yao Lu, Sun Yat-Sen Univ. (China); Jun Wei, Univ. of Michigan (United States); Zhenyu Qi, Sun Yat-Sen Univ. Cancer Ctr. (China); Ying Sun, Sun Yat-sen Univ. Cancer Ctr. (China)
Show Abstract
The efficacy of microaneurysms detection with and without vessel segmentation in color retinal images
Paper 11314-140
Author(s): Meysam Tavakoli, Indiana Univ.-Purdue Univ. Indianapolis (United States); Mahdieh Nazar, Shahid Beheshti Univ. of Medical Sciences (Iran, Islamic Republic of); Alireza Mehdizadeh, Shiraz Univ. of Medical Sciences (Iran, Islamic Republic of)
Show Abstract
Using an attention-based multi-scale fully convolutional network to identify the cup-disc and nerve fiber layer defects for diagnosis glaucoma
Paper 11314-141
Author(s): Hong Kang, Xiaoxing Li, Beijing Shanggong Medical Letter Technology Co., Ltd. (China); Xiu Su, Tianjin Univ. (China)
Show Abstract
Benign and malignant thyroid classification using computed tomography radiomics
Paper 11314-142
Author(s): Xiuxiu He, Bang Jun Guo, Tonghe Wang, Yang Lei, Tian Liu, Emory Univ. (United States); Long Jiang Zhang, Nanjing Univ. (China); Xiaofeng Yang, Emory Univ. (United States)
Show Abstract
Automatic classification of carotid ultrasound images based on convolutional neural network
Paper 11314-143
Author(s): Yujiao Xia, Huazhong Univ. of Science and Technology (China)
Show Abstract
CAD system for automated detection of pre-cancerous and cancerous versus benign oral lesions based on multispectral autofluorescence endoscopy
Paper 11314-144
Author(s): Elvis Duran, Shuna Cheng, Rodrigo Cuenca, John Wright, Y.S. Lisa Cheng, Texas A&M Univ. (United States); Beena Ahmed, Texas A&M Univ. (Qatar); Javier Jo, The Univ. of Oklahoma (United States)
Show Abstract
Verification of accuracy of an algorithmic image-based dental pulp vitality test
Paper 11314-145
Author(s): Sarah Bi, Naval Medical Research Unit San Antonio (United States), The Univ. of Texas at Austin (United States); Laura Martinez , Justin Bequette, Andrew Peitzsch, William D'Angelo, Naval Medical Research Unit San Antonio (United States)
Show Abstract
Organ-at-Risk (OAR) segmentation in head and neck CT using U-RCNN
Paper 11314-146
Author(s): Yang Lei, Joseph M. Harms, Xue Dong, Tonghe Wang, Xiangyang Tang, David S. Yu, Jonathan J. Beitler, Walter J. Curran, Tian Liu, Xiaofeng Yang, Emory Univ. (United States)
Show Abstract
3D thyroid segmentation in CT using self-attention convolutional neural network
Paper 11314-147
Author(s): Bang Jun Guo, Yang Lei, Yingzi Liu, Tonghe Wang, Tian Liu, Walter J. Curran, Emory Univ. (United States); Longjiang Zhang, Nanjing Univ. (China); Xiaofeng Yang, Emory Univ. (United States)
Show Abstract
Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique
Paper 11314-148
Author(s): Masahiro Oda, Nagoya Univ. (Japan); Naoyuki Maeda, Osaka Univ. (Japan); Takefumi Yamaguchi, Tokyo Dental College, Ichikawa General Hospital (Japan); Hideki Fukuoka, Kyoto Prefectural Univ. of Medicine (Japan); Yuta Ueno, Univ. of Tsukuba (Japan); Kensaku Mori, Nagoya Univ. (Japan)
Show Abstract
Hyperparameter selection for ResNet classification of malignancy from thyroid ultrasound images
Paper 11314-149
Author(s): Joseph Cox, Michigan State Univ. (United States), Purdue Univ. (United States); Sydney Rubin, Joe Adams, Michigan State Univ. (United States); Carina Pereira, Manjiri Dighe, Univ. of Washington (United States); Adam Alessio, Michigan State Univ. (United States)
Show Abstract
Detecting age-related macular degeneration (AMD) biomarker images using MFCC and texture features
Paper 11314-150
Author(s): Yiyang Wang, Xufan Ma, Rob Weddell, DePaul Univ. (United States); Abum Okemgbo, Univ. of Pennsylvania (United States); David Rein, Duke Univ. (United States); Amani A. Fawzi, Northwestern Univ. (United States); Jacob Furst, Daniela Raicu, DePaul Univ. (United States)
Show Abstract
Computer-aided detection of benign from precancerous and cancerous oral lesions based on multispectral autofluorescence lifetime endoscopy
Paper 11314-151
Author(s): Rodrigo Cuenca Martinez, Elvis Duran-Sierra, Shuna Cheng, Texas A&M Univ. (United States); John Wright, Yi-Shing L. Cheng, Texas A&M Univ. College of Dentistry (United States); Beena Ahmed, Jim Ji, Texas A&M Univ. at Qatar (Qatar); Javier A. Jo, The Univ. of Oklahoma (United States)
Show Abstract
Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma
Paper 11314-152
Author(s): Daniela Schenone, Univ. degli Studi di Genova (Italy); Rita Lai, Istituto Superconduttori, Materiali Innovativi e Dispositivi (Italy); Michele Cea, Univ. degli Studi di Genova (Italy), IRCCS Ospedale Policlinico San Martino (Italy); Federica Rossi, Lorenzo Torri, Univ. degli Studi di Genova (Italy); Bianca Bignotti, Univ. degli Studi di Genova (Italy), IRCCS Ospedale Policlinico San Martino (Italy); Giulia Succio, IRCCS Ospedale Policlinico San Martino (Italy); Stefano Gualco, Alessio Conte, Univ. degli Studi di Genova (Italy); Alida Dominietto, IRCCS Ospedale Policlinico San Martino (Italy); Anna Maria Massone, Univ. degli Studi di Genova (Italy), Istituto Superconduttori, Materiali Innovativi e Dispositivi (Italy); Michele Piana, Univ. degli Studi di Genova (Italy), CNR-SPIN (Italy); Francesco Frassoni, Univ. degli Studi di Genova (Italy); Gianmario Sambuceti, Alberto S. Tagliafico, Univ. degli Studi di Genova (Italy), IRCCS Ospedale Policlinico San Martino (Italy)
Show Abstract
Automated discomfort detection for premature infants in NICU using time-frequency feature-images and CNNs
Paper 11314-153
Author(s): Yue Sun, Technische Univ. Eindhoven (Netherlands); Deedee Kommers, Máxima MC (Netherlands); Tao Tan, Technische Univ. Eindhoven (Netherlands); Wenjin Wang, Xi Long, Caifeng Shan, Philips Research (Netherlands); Carola van Pul, Máxima MC (Netherlands); Ronald M. Aarts, Technische Univ. Eindhoven (Netherlands); Peter Andriessen, Máxima MC (Netherlands); Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)
Show Abstract
Standardization of blood flow measurements by automated vascular analysis from power Doppler ultrasound scan
Paper 11314-154
Author(s): Yi Yin, Pádraig Looney, Sally Collins, Univ. of Oxford (United Kingdom)
Show Abstract
A system for the analysis, diagnosis, and prognosis of wound images
Paper 11314-155
Author(s): Khajista Nizam, Topu Biswas, Mohammad Faizal Ahmad Fauzi, Nurul Nadia Ahmad, Multimedia Univ. (Malaysia); Harikrishna K.R. Nair, Kuala Lumpur Hospital (Malaysia)
Show Abstract
Investigation of the accuracy of classifying coronary artery disease severity using machine learning with subdomain analysis of Fractional Flow Reserve diagnosis in patients
Paper 11314-156
Author(s): Alexander R. Podgorsak, Canon Stroke and Vascular Research Ctr. (United States), Univ. at Buffalo, The State Univ. of New York (United States); Kelsey N. Sommer, Canon Stroke and Vascular Research Ctr. (United States), Univ. at Buffalo, The State Univ. of New York (United States); Vijay Iyer, Canon Stroke and Vascular Research Ctr. (United States), Univ. at Buffalo, The State Univ. of New York (United States); Michael F. Wilson, Canon Stroke and Vascular Research Ctr. (United States), Univ. at Buffalo, The State Univ. of New York (United States); Frank J. Rybicki, Univ. of Cincinnati (United States); Dimitrios Mitsouras, Univ. of California, San Francisco (United States); Umesh Sharma, Canon Stroke and Vascular Research Ctr. (United States), Univ. at Buffalo, The State Univ. of New York (United States); Kanako K. Kumamaru, Juntendo Univ. (Japan); Erin Angel, Canon Medical Systems USA, Inc. (United States); Ciprian N. Ionita, Canon Stroke and Vascular Research Ctr (United States), Univ. at Buffalo, The State Univ. of New York (United States)
Show Abstract
Investigation of sex hormones on the early diagnosis of Schizophrenia
Paper 11314-157
Author(s): Yaping Wang, Peilun Song, Zhengzhou Univ. (China); Xiujuan Geng, The Chinese Univ. of Hong Kong (China)
Show Abstract
Mask R-CNN based coronary artery segmentation in coronary computed tomography angiography
Paper 11314-158
Author(s): Yabo Fu, Bang Jun Guo, Yang Lei, Tian Liu, Walter J. Curran, Emory Univ. (United States); Long Jiang Zhang, Nanjing Univ. (China); Xiaofeng Yang, Emory Univ. (United States)
Show Abstract
Fully automated spectral envelope and peak velocity detection from Doppler Echocardiography images
Paper 11314-159
Author(s): Ghada Zamzmi, Wen Li, Li-Yueh Hsu, Vandana Sachdev, Sameer Antani, National Institutes of Health (United States)
Show Abstract
ComboNet: combined 2D and 3D architecture for aorta segmentation
Paper 11314-160
Author(s): Orhan Akal, Florida State Univ. (United States); Zhigang Peng, Gerardo Hermosillo Valadez, Siemens Healthineers (United States)
Show Abstract
Tuesday 18 February Show All Abstracts
Session 8:
Breast
Tuesday 18 February 2020
8:00 AM - 9:40 AM
Location: Salon B
Session Chairs:
Shandong Wu, Univ. of Pittsburgh (United States) ;
Karen Drukker, The Univ. of Chicago Medicine (United States)
Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks
Paper 11314-39
Author(s): Ravi K. Samala, Heang-Ping Chan, Lubomir Hadjiiski, Sathvik Koneru, Univ. of Michigan (United States)
Show Abstract
Architectural distortion detection approach guided by mammary gland spatial pattern in digital breast tomosynthesis
Paper 11314-40
Author(s): Yue Li, Zheng Xie, Sun Yat-Sen Univ. (China); Zilong He, Nanfang Hospital of the Southern Medical Univ. (China); Xiangyuan Ma, Sun Yat-Sen Univ. (China); Yanhui Guo, Univ. of Illinois at Springfield (United States); Weiguo Chen, Nanfang Hospital of the Southern Medical Univ. (China); Yao Lu, Sun Yat-Sen Univ. (China)
Show Abstract
Simulating breast mammogram using conditional generative adversarial network: application towards finding mammographically-occult cancer
Paper 11314-41
Author(s): Juhun Lee, Robert Nishikawa, Univ. of Pittsburgh (United States)
Show Abstract
Weakly-supervised us breast tumor characterization and localization with a box convolution network
Paper 11314-42
Author(s): Chanho Kim, Kyungpook National Univ. (Korea, Republic of); Won Hwa Kim, Hye Jung Kim, Kyungpook National Univ. Chilgok Hospital (Korea, Republic of); Jaeil Kim, Kyungpook National Univ. (Korea, Republic of)
Show Abstract
Performance comparison of different loss functions for digital breast tomosynthesis classification using 3D deep learning model
Paper 11314-43
Author(s): Emine Doganay, Univ. of Pittsburgh (United States); Yahong Luo, Liaoning Cancer Hospital and Institute (China); Long Gao, Univ. of Pittsburgh (United States); Puchen Li, Liaoning Cancer Hospital and Institute (China); Wendie A. Berg, Univ. of Pittsburgh (United States); Shandong Wu, Univ of Pittsburgh (United States)
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Session 9:
Chest II, Lymph Nodes
Tuesday 18 February 2020
10:10 AM - 12:10 PM
Location: Salon B
Session Chairs:
Ronald M. Summers, National Institutes of Health Clinical Ctr. (United States) ;
Fujii Hiroshi, Hokkaido Univ. (Japan)
Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks
Paper 11314-44
Author(s): Tobias Klinder, Rafael Wiemker, Heike Carolus, Tom Brosch, Philips Research (Germany); Andra-Iza Iuga, Anna Höink, David Maintz, Michael Püsken, Univ. zu Köln (Germany); Frank Thiele, Philips Healthcare (Germany)
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Feasibility of end-to-end trainable two-stage U-Net for detection of axillary lymph nodes in Contrast-Enhanced CT based on sparse annotations
Paper 11314-45
Author(s): Hidir Cem Altun, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany), Jacobs Univ. Bremen (Germany); Grzegorz Chlebus, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany), Radboud Univ. Medical Ctr. (Netherlands); Colin Jacobs, Radboud Univ. Medical Ctr. (Netherlands); Hans Meine, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany), Univ. Bremen (Germany); Bram van Ginneken, Radboud Univ. Medical Ctr. (Netherlands), Fraunhofer-Institut für Digitale Medizin MEVIS (Germany); Horst K. Hahn, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
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Lung vessel suppression and its effect on nodule detection in chest CT scans
Paper 11314-46
Author(s): Xiaomeng Gu, Shanghai Jiao Tong Univ. (China); Weiyang Xie, United Imaging Healthcare Co., Ltd. (China); Qiming Fang, Jun Zhao, Shanghai Jiao Tong Univ. (China); Qiang Li, Huazhong Univ. of Science and Technology (China)
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Hybrid deep-learning model for volume segmentation of lung nodules in CT images
Paper 11314-47
Author(s): Yifan Wang, Chuan Zhou, Heang-Ping Chan, Lubomir M. Hadjiiski, Jun Wei, Aamer Chughtai, Ella A. Kazerooni, Univ. of Michigan (United States)
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Machine learning and deep learning approaches for classification of sub-cm lung nodules in CT scans
Paper 11314-48
Author(s): Rohan Abraham, Ian Janzen, Saeed Seyyedi, Sukhinder Khattra, John Mayo, Ren Yuan, Renelle Myers, Stephen Lam, Calum E. MacAulay, BC Cancer Research Ctr. (Canada)
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EDICNet: an end-to-end detection and interpretable classification network for pulmonary nodules on computed tomography
Paper 11314-49
Author(s): Yannan Lin, William Hsu, Denise R. Aberle, Leihao Wei, Simon X. Han, Univ. of California, Los Angeles (United States)
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Lunch Break 12:10 PM - 1:20 PM
Session 10:
Keynote and Methodology
Tuesday 18 February 2020
1:20 PM - 3:00 PM
Location: Salon B
Session Chairs:
Horst K. Hahn, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany) ;
Maciej A. Mazurowski, Duke Univ. (United States)
Will AI make me a better doctor? (Keynote Presentation)
Paper 11314-50
Author(s): Jonathan Wiener, Florida Atlantic Univ. (United States)
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Weakly-supervised lesion segmentation on CT scans using co-segmentation
Paper 11314-51
Author(s): Vatsal Agarwal, National Institutes of Health (United States), Univ. of Maryland, College Park (United States); Youbao Tang, National Institutes of Health (United States); Jing Xiao, China Ping An Insurance (Group) Co., Ltd. (China); Ronald M. Summers, National Institutes of Health (United States)
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Quality controlled segmentation to aid disease detection
Paper 11314-138
Author(s): Mehdi Moradi, KenC. L. Wong, Alexandros Karargyris, Tanveer Syeda-Mahmood, IBM Research - Almaden (United States)
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Session 11:
Head and Neck, Eye
Tuesday 18 February 2020
3:30 PM - 4:50 PM
Location: Salon B
Session Chairs:
Maciej A. Mazurowski, Duke Univ. (United States) ;
Carol L. Novak, Siemens Healthineers (United States)
Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection
Paper 11314-53
Author(s): Marcel Bengs, Technische Univ. Hamburg-Harburg (Germany); Stephan Westermann, Rheinische Friedrich-Wilhelms-Univ. Bonn (Germany); Nils Gessert, Technische Univ. Hamburg-Harburg (Germany); Dennis Eggert, Universitätsklinikum Hamburg-Eppendorf (Germany); Andreas O. H. Gerstner, Klinikum Braunschweig GmbH (Germany); Nina A. Müller, Rheinische Friedrich-Wilhelms-Univ. Bonn (Germany); Christian Betz, Wiebke Laffers, Universitätsklinikum Hamburg-Eppendorf (Germany); Alexander Schlaefer, Technische Univ. Hamburg-Harburg (Germany)
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Decision fusion on image analysis and tympanometry to detect eardrum abnormalities
Paper 11314-54
Author(s): Hamidullah Binol, Wake Forest Ctr. for Biomedical Informatics (United States); Aaron C. Moberly, The Ohio State Univ. (United States); M. Khalid Khan Niazi, Wake Forest Ctr. for Biomedical Informatics (United States); Garth Essig, The Ohio State Univ. (United States); Jay Shah, Case Western Reserve Univ. (United States); Charles Elmaraghy, Theodoros Teknos, Nazhat Taj-Schaal, Lianbo Yu, The Ohio State Univ. (United States); Metin N. Gurcan, Wake Forest Ctr. for Biomedical Informatics (United States)
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Direct classification of type 2 diabetes from retinal fundus images in a population-based sample from The Maastricht Study
Paper 11314-55
Author(s): Friso G. Heslinga, Josien P. W. Pluim, Technische Univ. Eindhoven (Netherlands); A.J.H.M. Houben, Miranda T. Schram, Ronald M. A. Henry, Maastricht Univ. Medical Ctr. (Netherlands); Coen D.A. Stehouwer, Maastricht Univ. Medical Center (Netherlands); Marleen J. van Greevenbroek, Maastricht Univ. Medical Ctr. (Netherlands); Tos T.J.M. Berendschot, Technische Univ. Eindhoven (Netherlands), Maastricht Univ. Medical Center (Netherlands); Mitko Veta, Technische Univ. Eindhoven (Netherlands)
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Segmentation of retinal low-cost optical coherence tomography images using deep learning
Paper 11314-56
Author(s): Timo Kepp, Univ. zu Lübeck (Germany); Helge M. Sudkamp, Medizinisches Laserzentrum Lübeck GmbH (Germany); Claus von der Burchard, Hendrik Schenke, Christian-Albrechts-Univ. zu Kiel (Germany); Peter Koch, Gereon M. Hüttmann, Medizinisches Laserzentrum Lübeck GmbH (Germany); Johann Roider, Christian-Albrechts-Univ. zu Kiel (Germany); Mattias P. Heinrich, Heinz Handels, Univ. zu Lübeck (Germany)
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Session WK4:
Workshop: Simulated Tumor Board: Brain and Breast
Tuesday 18 February 2020
5:00 PM - 7:00 PM
Location: Salon B

This workshop will present two example clinical cases, one breast cancer case and one brain cancer case. A multi-disciplinary team will discuss the case, the imaging information, pathology, and treatment options. The workshop will mimic the format of a standard clinical tumor board process with time for Q&A at the end.

Moderator:
Kristy Brock, PhD, DABR, FAAPM
Professor, Department of Imaging Physics and Department of Radiation Physics, Univ. of Texas MD Anderson Cancer Center (United States)

Breast Panel Speakers:
Simona Shaitelman, Univ. of Texas MD Anderson Cancer Ctr., Radiation Oncology (United States)
Isabelle Bedrosian, Univ. of Texas MD Anderson Cancer Ctr., Surgery (United States)
Jennifer Litton, Univ. of Texas MD Anderson Cancer Ctr., Medical Oncology (United States)
Wei Yang, Univ. of Texas MD Anderson Cancer Ctr., Diagnostic Radiology (United States)
Alejandro Contreras, Univ. of Texas MD Anderson Cancer Ctr., Pathology (United States)

Brain Panel Speakers:
Caroline Chung, Univ. of Texas MD Anderson Cancer Ctr., Radiation Oncology (United States)
Jeff Weinberg, Univ. of Texas MD Anderson Cancer Ctr., Surgery (United States)
Melissa Chen, Univ. of Texas MD Anderson Cancer Ctr., Diagnostic Radiology (United States)
Jason Huse, Univ. of Texas MD Anderson Cancer Ctr., Pathology (United States)
Wednesday 19 February Show All Abstracts
Session 12:
Novel Applications
Wednesday 19 February 2020
8:00 AM - 9:40 AM
Location: Salon B
Session Chairs:
Lubomir M. Hadjiiski, Michigan Medicine (United States) ;
Sameer K. Antani, U.S. National Library of Medicine (United States)
Attention-guided classification of abnormalities in semi-structured Computed Tomography reports
Paper 11314-57
Author(s): Khrystyna Faryna, Univ. de Girona (Spain), Univ. de Bourgogne (France), Univ. degli Studi di Cassino e del Lazio Meridionale (Italy); Fakrul I. Tushar, Rui Hou, Carl E. Ravin Advanced Imaging Labs., Duke Univ. Medical Ctr. (United States); Geoffrey D. Rubin, Duke Univ. School of Medicine (United States); Joseph Y. Lo, Carl E. Ravin Advanced Imaging Labs., Duke Univ. Medical Ctr. (United States)
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Cascading YOLO: automated malaria parasite detection for plasmodium vivax in thin blood smears
Paper 11314-58
Author(s): Feng Yang, Nicolas Quizon, National Institutes of Health (United States); Kamolrat Silamut, Richard Maude, Mahidol Univ. (Thailand); Stefan Jaeger, Sameer Antani, Lister Hill National Ctr. for Biomedical Communications, U.S. National Library of Medicine (United States)
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Segmentation of uterus and placenta in MR images using a fully convolutional neural network
Paper 11314-59
Author(s): Maysam Shahedi, James D. Dormer, Anusha Devi Tensingh Rajan Thanga Kani, The Univ. of Texas at Dallas (United States); Quyen N. Do, Yin Xi, Matthew A. Lewis, Ananth J. Madhuranthakam, Diane M. Twickler, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); Baowei Fei, The Univ. of Texas at Dallas (United States), The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
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A multi-stage fusion strategy for multi-scale GLCM-CNN model in differentiating malignant from benign polyps
Paper 11314-60
Author(s): Jiaxing Tan, Zhengrong Liang, Shu Zhang, Weiguo Cao, Yongfeng Gao, Stony Brook Univ. (United States); Lihong C. Li, Yumei Huo, The City Univ. of New York (United States), College of Staten Island (United States)
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Convolutional neural network-based decision support system for bladder cancer staging in CT urography: decision threshold estimation and validation
Paper 11314-61
Author(s): Daniel H. Chapman-Sung, Lubomir Hadjiiski, Dhanuj Gandikota, Heang-Ping Chan, Ravi Samala, Elaine M. Caoili, Richard H. Cohan, Alon Weizer, Ajjai Alva, Chuan Zhou, Univ. of Michigan (United States)
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Session 13:
Neuro II
Wednesday 19 February 2020
10:10 AM - 12:10 PM
Location: Salon B
Session Chairs:
Pallavi Tiwari, Case Western Reserve Univ. (United States) ;
Hayit Greenspan, Tel Aviv Univ. (Israel)
Attention-deficit/hyperactivity disorder prediction using graph convolutional networks
Paper 11314-62
Author(s): Seyed Saman Saboksayr, John J. Foxe, Axel Wismüller, Univ. of Rochester (United States)
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An extended-2D CNN for multiclass Alzheimer's Disease diagnosis through structural MRI
Paper 11314-63
Author(s): Mariana Eugênia de Carvalho Pereira, Irene Fantini, Roberto A. Lotufo, Letícia Rittner, Univ. Estadual de Campinas (Brazil)
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Combining deep and hand-crafted MRI features for identifying gender-specific differences in ASD versus normal controls
Paper 11314-65
Author(s): Yashas Hiremath, Marwa Ismail, Ruchika Verma, Ashish Gupta, Pallavi Tiwari, Case Western Reserve Univ. (United States)
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Multi-modal deep learning for predicting progression of Alzheimer's disease using bi-linear shake fusion
Paper 11314-66
Author(s): Tsubasa Goto, Caihua Wang, Yuanzhong Li, FUJIFILM Corp. (Japan); Yukihiro Tsuboshita, Fuji Xerox Co., Ltd. (Japan)
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Large-scale Extended Granger Causality (lsXGC) for classification of Autism Spectrum Disorder from resting-state functional MRI
Paper 11314-64
Author(s): Axel Wismüller, John J. Foxe, Seyed Saman Saboksayr, Univ. of Rochester (United States)
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Prognostic power of the human psoas muscles FDG metabolism in amyotrophic lateral sclerosis
Paper 11314-67
Author(s): Rita Lai, Istituto Superconduttori, Materiali Innovativi e Dispositivi (Italy); Daniela Schenone, Univ. degli Studi di Genova (Italy); Gianmario Sambuceti, Univ. degli Studi di Genova (Italy), IRCCS Ospedale Policlinico San Martino Genova (Italy); Anna Maria Massone, Istituto Superconduttori, Materiali Innovativi e Dispositivi (Italy), Univ. degli Studi di Genova (Italy); Michele Piana, Univ. degli Studi di Genova (Italy), Istituto Superconduttori, Materiali Innovativi e Dispositivi, Consiglio Nazionale delle Richerche (Italy); Adriano Chiò, ALS Ctr. Rita Levi Montalcini, Univ. degli Studi di Torino (Italy), AUO Città della Salute e della Scienza Torino (Italy); Claudia Caponnetto, ALS Ctr. Rita Levi Montalcini, Univ. degli Studi di Torino (Italy); Angelina Cistaro, PET Ctr., IRMET SpA Affidea Torino (Italy); Matteo Bauckneht, Vanessa Cossu, Univ. degli Studi di Genova (Italy); Silvia Morbelli, IRCCS Ospedale Policlinico San Martino (Italy); Cecilia Marini, Istituto di Bioimmagini e Fisiologia Molecolare (Italy), IRCCS Ospedale Policlinico San Martino (Italy)
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