Town and Country Resort and Convention Center
San Diego, California, United States
14 - 18 February 2021
Conference 11597
Computer-Aided Diagnosis
Conference Committee
Monday 30 November Show All Abstracts
Session 1: Lung I
Role of standard and soft tissue chest radiography images in COVID-19 diagnosis using deep learning
Paper 11597-1
Author(s): Qiyuan Hu, Karen Drukker, Maryellen Giger, The Univ. of Chicago (United States)
Show Abstract
Deep radiomics: deep learning on radiomics texture images
Paper 11597-66
Author(s): Rahul Paul, Sherzod Kariev, Dmitry Cherezov, Univ. of South Florida (United States); Matthew Schabath, Robert Gillies, Moffitt Cancer Ctr. (United States); Lawrence Hall, Dmitry Goldgof, Univ. of South Florida (United States)
Show Abstract
COVID-19 pneumonia diagnosis using chest x-ray radiograph (CXR) and deep learning
Paper 11597-3
Author(s): Dalton Griner, Ran Zhang, Xin Tie, Chengzhu Zhang, John Garrett, Ke Li, Guang-Hong Chen, Univ. of Wisconsin-Madison (United States)
Show Abstract
Automatic localization of lung opacity in chest CT images - a real-world study
Paper 11597-4
Author(s): Yiting Xie, IBM Corp. (United States); Deepta Rajan, IBM Research - Almaden (United States); Larissa Schudlo, Yusuke Takeuchi, Benedikt Graf, Adam Coy, IBM Corp. (United States); Mohammadreza Negahdar, Vandana Mukherjee, David Beymer, IBM Research - Almaden (United States); Arun Krishnan, IBM Corp. (United States)
Show Abstract
Transferring CT image biomarkers from fibrosing idiopathic interstitial pneumonia to COVID-19 analysis
Paper 11597-5
Author(s): Catalin Fetita, SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris (France); Simon Rennotte, Marjorie Latrasse, Télécom SudParis (France); Hilario Nunes, Pierre-Yves Brillet, Univ. Paris 13 (France)
Show Abstract
Evolution of chest radiograph radiomics and association with respiratory and inflammatory parameters in COVID-19 patients undergoing prone ventilation: preliminary findings
Paper 11597-6
Author(s): Connor Cowan, Stony Brook Univ. (United States); Joseph Bae, Stony Brook Medicine (United States); Gagandeep Singh, Newark Beth Israel Medical Ctr. (United States); Rohit Khullar, Stony Brook Medicine (United States); Shrey Shah, RWJBarnabas Health (United States); Nikhil Madan, Newark Beth Israel Medical Ctr. (United States); Prateek Prasanna, Stony Brook Univ. (United States)
Show Abstract
Classification of COVID-19 in chest radiographs: assessing the impact of imaging parameters using clinical and simulated images
Paper 11597-7
Author(s): Rafael Fricks, U.S. Dept. of Veterans Affairs (United States), Duke Univ. (United States); Ehsan Abadi, Francesco Ria, Ehsan Samei, Duke Univ. (United States)
Show Abstract
Session Plen: Awards and Plenary Session
Session Chairs:
Metin N. Gurcan, Wake Forest Baptist Medical Ctr. (United States) ;
Robert M. Nishikawa, Univ. of Pittsburgh (United States)

4:00 pm - Welcome and new SPIE Fellows Acknowledgements

4:15 pm - Best Student Paper Awards Announcements

The first place winner and runner up of the Robert F. Wagner All-Conference Student Paper Award will be announced.

4:20 pm - SPIE Harrison H. Barrett Award in Medical Imaging

This award will be presented in recognition of outstanding accomplishments in medical imaging.

4:30 pm - Plenary Presentation

Biophotonics Solutions to Global Health Challenges (Plenary Presentation)
Paper 11596-300
Author(s): Rebecca R. Richards-Kortum, Rice Univ. (United States)
Show Abstract
Session 2: Breast I
Intrinsic radiomics phenotypes of DCI from breast DCE-MRI: demonstrating feasibility in interim analysis of the ECOG-ACRIN E4112 trial
Paper 11597-8
Author(s): J. Vivian Belenky, Rhea Chitalia, Univ. of Pennsylvania (United States); Debosmita Biswas, Univ. of Washington (United States); Constantine Gatsonis, Brown Univ. (United States); Jennifer Xiao, Michael Hirano, Univ. of Washington (United States); Sunil S. Badve, Indiana Univ. (United States); Seema A. Khan, Northwestern Univ. (United States); Constance D. Lehman, Massachusetts General Hospital (United States); Justin Romanoff, Brown Univ. (United States); Antonio C. Wolff, Johns Hopkins Univ. (United States); Kathy D. Miller, Indiana Univ. (United States); Joseph A. Sparano, Montefiore Medical Ctr. (United States); Christopher Comstock, Memorial Sloan-Kettering Cancer Ctr. (United States); Savannah C. Partridge, Univ. of Washington Medical Ctr. (United States); Habib Rahbar, Univ. of Washington (United States); Despina Kontos, Univ. of Pennsylvania (United States)
Show Abstract
Electronic removal of lesions for more robust BPE scoring on breast DCE-MRI
Paper 11597-9
Author(s): Lindsay Douglas, The Univ. of Chicago (United States); Deepa Sheth, The Univ. of Chicago Medical Ctr. (United States); Maryellen Giger, The Univ. of Chicago (United States)
Show Abstract
Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI
Paper 11597-10
Author(s): Roma Bhattacharjee, Lindsay Douglas, Karen Drukker, Qiyuan Hu, Jordan Fuhrman, Deepa Sheth, Maryellen L. Giger, The Univ. of Chicago (United States)
Show Abstract
Sparse analysis of deep features for characterization of breast Masses
Paper 11597-11
Author(s): Sokratis Makrogiannis, Chelsea Harris, Delaware State Univ. (United States)
Show Abstract
Detecting invasive breast carcinoma on dynamic contrast-enhanced MRI
Paper 11597-12
Author(s): Stefania L. Moroianu, Mirabela Rusu, Stanford Univ. (United States)
Show Abstract
Session 3: Abdomen I
Deformable MRI-CT liver image registration using Convolutional Neural Network with modality independent neighborhood descriptors
Paper 11597-13
Author(s): Yabo Fu, Yang Lei, Tonghe Wang, Jun Zhou, Walter Curran, Pretesh Patel, Tian Liu, Xiaofeng Yang, The Winship Cancer Institute of Emory Univ. (United States)
Show Abstract
Deep learning-based deformable MRI-CBCT registration of male pelvic region
Paper 11597-14
Author(s): Shadab Momin, Yang Lei, Tonghe Wang, Yabo Fu, Pretesh Patel, Ashesh B. Jani, Walter Curran, Tian Liu, Xiaofeng Yang, The Winship Cancer Institute of Emory Univ. (United States)
Show Abstract
3D deep learning for computer-aided detection of serrated polyps in CT colonography
Paper 11597-15
Author(s): Janne J. Näppi, Massachusetts General Hospital (United States); Tomoki Uemura, Kyushu Institute of Technology (Japan); Perry Pickhardt, David H. Kim, Univ. of Wisconsin-Madison (United States); Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Show Abstract
Intensity normalization of prostate MRIs using conditional generative adversarial networks for cancer detection
Paper 11597-16
Author(s): Thomas DeSilvio, Case Western Reserve Univ. (United States); Stefania Moroianu, Indrani Bhattacharya, Arun Seetharaman, Geoffrey Sonn, Mirabela Rusu, Stanford Univ. (United States)
Show Abstract
Ovarian assessment using deep learning based 3D ultrasound super resolution
Paper 11597-17
Author(s): Saumya Gupta, Venkata Suryanarayana K., Srinivas R. Kudavelly, SAMSUNG R&D Institute India, Bangalore (India)
Show Abstract
Abdominal CT urography kidney segmentation using spatiotemporal fully convolutional network
Paper 11597-18
Author(s): Wankang Zeng, Wenkang Fan, Zhuohui Zheng, Rong Chen, Xiamen Univ. (China); Zengqin Liu, Shenzhen People's Hospital (China); Yinran Chen, Xiongbiao Luo, Xiamen Univ. (China)
Show Abstract
Tuesday Poster Viewing

Posters will be on display Tuesday and Wednesday with extended viewing until 9:00 pm on Tuesday. The poster session with authors in attendance will be Wednesday 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 PST
Session 4: Cardiovascular and Ophthalmology
Multi-part segmentation of the thoracic aorta using an attention-gated U-Net
Paper 11597-19
Author(s): Jiayang Zhong, Zhangxing Bian, Univ. of Michigan (United States); Charles R. Hatt, Imbio, LLC (United States); Nicholas S. Burris, Univ. of Michigan (United States)
Show Abstract
Detection of left bundle branch block and obstructive coronary artery disease from myocardial perfusion scintigraphy using deep neural networks
Paper 11597-20
Author(s): Ida Arvidsson, Niels Christian Overgaard, Lund Univ. (Sweden); Anette Davidsson, Jeronimo Frias Rose, Linköping Univ. (Sweden); Kalle Åström, Lund Univ. (Sweden); Miguel Ochoa Figueroa, Linköping Univ. (Sweden); Anders Heyden, Lund Univ. (Sweden)
Show Abstract
Modality agnostic intracranial aneurysm detection through supervised vascular surface classification
Paper 11597-21
Author(s): Žiga Bizjak, Boštjan Likar, Franjo Pernuš, Žiga Špiclin, Univ. of Ljubljana (Slovenia)
Show Abstract
Automated multi-channel segmentation for the 4D myocardial velocity mapping cardiac MR
Paper 11597-22
Author(s): Yinzhe Wu, National Heart and Lung Institute, Imperial College London (United Kingdom); Suzan Hatipoglu, Royal Brompton Hospital (United Kingdom); Diego Alonso-Álvarez, Imperial College London (United Kingdom); Peter Gatehouse, David Firmin, Jennifer Keegan, Guang Yang, National Heart and Lung Institute, Imperial College London (United Kingdom), Royal Brompton Hospital (United Kingdom)
Show Abstract
Automatic detection of ellipsoid zone loss due to Hydroxychloroquine retinal toxicity from SD-OCT imaging
Paper 11597-23
Author(s): Tharindu S. De Silva, Gopal Jayakar, Peyton Grisso, Emily Y. Chew, National Eye Institute (United States); Nathan Hotaling, National Ctr. for Advancing Translational Sciences (United States); Catherine A. Cukras, National Eye Institute (United States)
Show Abstract
Session 5: Grand Challenge: Abnormality Detection in Digital Breast Tomosynthesis

Overview:
We ask for participation in the DBTex Grand Challenge by submitting algorithms for the detection of breast lesions on digital breast tomosynthesis images. The results of the competition will be announced at the special session of the SPIE Medical Imaging 2021 conference. Participants in the first DBTex Grand Challenge are encouraged to submit their work for peer review to the SPIE’s Journal of Medical Imaging.

Organizers:
This challenge is organized by SPIE (the international society for optics and photonics), The American Association of Physicists in Medicine (AAPM), the National Cancer Institute (NCI), and Duke Center for Artificial Intelligence in Radiology (DAIR).

Important Dates:
Release date of training set cases with truth: November 16, 2020
Release date of validation set cases: December 6, 2020
Release date of test set cases: December 28, 2020
Submission date for participants’ test set output: January 18, 2021
Challenge results released to participants: January 25, 2021
SPIE Medical Imaging Symposium: February 14–18, 2021
Session WK3: Workshop: Live Demonstrations
Session Chairs:
Karen Drukker, The Univ. of Chicago Medicine (United States) ;
Lubomir M. Hadjiiski, Michigan Medicine (United States) ;
Horst Karl Hahn, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)

Workshop Chairs:
Dr. Lubomir Hadjiiski, Univ. of Michigan Health System (United States)
Dr. Karen Drukker, Univ. of Chicago (United States)
Dr. Horst Hahn, Fraunhofer MEVIS and Jacobs Univ. Bremen (Germany)

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 15, 2021: Deadline for submission
  • January 22, 2021: Notification of acceptance
  • January 29, 2021: 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, Karen Drukker, and Horst Hahn:
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:
  • Teams from academia (universities, university medical centers, research organizations), government, and industry are invited to participate in this year’s workshop. Demonstrations should be scientific and not commercial in nature; demonstration of research prototypes is highly encouraged.
  • After you submit a description of your proposed demonstration, you will receive a confirmation by e-mail.
  • The organizers will accept teams for demonstrations based on the quality of the provided description. If there are more proposals than presentation slots in the workshop, organizers will also strive to select a representative mix of applications.
  • Notification of acceptance/rejection of your demonstration for the Workshop will be emailed about 3 weeks before the conference (see ‘important dates’ above).
  • For demonstrations accepted for presentation at the Live Demonstration Workshop:
    • The accepted demonstrations will be listed online in the workshop program.
    • All teams need to provide one or two slides describing their system before the conference (see ‘important dates’ above) from which the opening presentation will be compiled.
    • In the case of in person SPIE Medical Imaging meeting, 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.
    • In the case of virtual SPIE Medical Imaging meeting, each team will demonstrate their tool over internet. More details will be provided in the future.
    • 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.
Session 6: Lung II
Toward the mitigation of variability of lung nodule radiomic features in CT scans
Paper 11597-24
Author(s): Nastaran Emaminejad, Muhammad Wasil Wahi-Anwar, Grace Kim, William Hsu, Matthew Brown, Michael McNitt-Gray, Univ. of California, Los Angeles (United States)
Show Abstract
Lung nodule malignancy prediction in chest CT scans based on a CNN model with auxiliary task learning
Paper 11597-25
Author(s): Xiaomeng Gu, Shanghai Jiao Tong Univ. (China); Weiyang Xie, United Imaging Healthcare Co., Ltd. (China)
Show Abstract
Traction bronchiectasis identification and quantitative biomarkers for follow up in nonspecific interstitial pneumonia
Paper 11597-26
Author(s): Marjorie Latrasse, Simon Rennotte, Télécom SudParis (France); Pierre-Yves Brillet, Univ. Paris 13 (France); Sylvain Marchand-Adam, Univ. de Tours (France); Catalin Fetita, Télécom SudParis, Institut Mines-Télécom (France)
Show Abstract
Region of interest discovery using discriminative concrete autoencoder for COVID-19 lung CT images
Paper 11597-27
Author(s): Yupei Zhang, Mingquan Lin, Walter Curran, Tian Liu, Xiaofeng Yang, The Winship Cancer Institute of Emory Univ. (United States)
Show Abstract
Detecting COVID-19 infected pneumonia from X-ray images using a deep learning model with image preprocessing algorithm
Paper 11597-28
Author(s): Morteza Heidari, Seyedehnafiseh Mirniaharikandehei, The Univ. of Oklahoma (United States); Abolfazl Zargari Khuzani, Univ. of California, Santa Cruz (United States); Gopichandh Danala, Yuchen Qiu, Bin Zheng, The Univ. of Oklahoma (United States)
Show Abstract
Session 7: Breast II
An improved mammography malignancy model with self-supervised learning
Paper 11597-29
Author(s): Yen Nhi Truong Vu, Trevor Tsue, Jason Su, Sadanand Singh, Whiterabbit.ai (United States)
Show Abstract
Case-based diagnostic classification repeatability using radiomic features extracted from full-field digital mammography images of breast lesions
Paper 11597-30
Author(s): Paul Amstutz, Wheaton College (United States), The Univ. of Chicago (United States); Karen Drukker, Hui Li, 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
Estimating near term breast cancer risk from sequential mammograms using deep learning, Radon Cumulative Distribution Transform, and a clinical risk factor: preliminary analysis
Paper 11597-31
Author(s): Juhun Lee, Robert M. Nishikawa, Andriy Bandos, Margarita Zuley, Univ. of Pittsburgh (United States)
Show Abstract
Parenchymal field effect analysis for breast cancer risk assessment: evaluation of FFDM radiomic similarity
Paper 11597-32
Author(s): Natalie Baughan, Hui Li, Li Lan, Chun-Wai Chan, The Univ. of Chicago (United States); Matthew Embury, Gary Whitman, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States); Randa El-Zein, Houston Methodist Research Institute (United States); Isabelle Bedrosian, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States); Maryellen Giger, The Univ. of Chicago (United States)
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 11597-33
Author(s): Xiangrong Zhou, Seiya Yamagishi, Takeshi Hara, Hiroshi Fujita, Gifu Univ. (Japan)
Show Abstract
Multi-Task Learning to incorporate clinical knowledge into deep learning for breast cancer diagnosis
Paper 11597-34
Author(s): Giacomo Nebbia, Dooman Arefan, Shandong Wu, Univ. of Pittsburgh (United States)
Show Abstract
Lunch Break 12:10 PM - 1:20 PM PST
Session 8: Musculoskeletal
Medical knowledge-guided Deep Curriculum Learning for elbow fracture diagnosis from X-ray images
Paper 11597-35
Author(s): Jun Luo, Gene Kitamura, Shandong Wu, Univ. of Pittsburgh (United States)
Show Abstract
Assessment of bone fragility in projection images using radiomic features
Paper 11597-36
Author(s): Qian Cao, Nicholas Petrick, Stephanie Coquia, Kenny H. Cha, Rongping Zeng, Keith Wear, Berkman Sahiner, Qin Li, U.S. Food and Drug Administration (United States)
Show Abstract
Two-stage meniscus segmentation framework integrating multiclass localization network and adversarial learning-based segmentation network in knee MR images
Paper 11597-37
Author(s): Uju Jeon, Hyeonjin Kim, Helen Hong, Seoul Women's Univ. (Korea, Republic of); Joon Ho Wang, SAMSUNG Medical Ctr. (Korea, Republic of)
Show Abstract
Deep learning segmentation of intervertebral discs in the cervical spine
Paper 11597-38
Author(s): Ien Li, Kirstin Cook, Michael Le, Bilwaj K. Gaonkar, Luke Macyszyn, Univ. of California, Los Angeles (United States)
Show Abstract
Deep learning based risk stratification for treatment management of multiple myeloma with sequential MRI scans
Paper 11597-39
Author(s): Chuan Zhou, Heang-Ping Chan, Qian Dong, Lubomir M. Hadjiiski, Michigan Medicine (United States)
Show Abstract
Session 9: Keynote and Pediatric/Fetal Applications
Session Chairs:
Maciej A. Mazurowski, Duke Univ. (United States) ;
Karen Drukker, The Univ. of Chicago Medicine (United States)
To be determined (Keynote Presentation)
Paper 11597-500
Author(s):
Show Abstract
Video-based infant monitoring using a CNN-LSTM scheme
Paper 11597-40
Author(s): Lan Min, Yue Sun, Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)
Show Abstract
Automatic fetal presentation diagnosis from ultrasound images for rural zones: head location as an indicator for fetal presentation
Paper 11597-41
Author(s): Junior Arroyo Barboza, Ana Cecilia Saavedra Bazan, Pontificia Univ. Católica del Perú (Peru); Lorena Tamayo, Miguel Egoavil, Medical Innovation & Technology S.A.C. (Peru); Berta Ramos, Benjamin Castaneda, Pontificia Univ. Católica del Perú (Peru)
Show Abstract
Session PSWed: Wednesday Poster Session

All symposium attendees – You are invited to attend the evening Wednesday Poster Session to view the high-quality posters and engage the authors in discussion. Enjoy light refreshments while networking with colleagues in your field. Authors may set up their posters starting Tuesday 16 February after 12:00 pm*.

*In order to be fully considered for a Poster Award, it is recommended to have your poster up as soon as possible on the above listed setup day. Posters should remain on display until the end of the Poster Session on Wednesday.

Attendees are required to wear their conference registration badges to access the Poster Session.

Posters that are not set up by the 4:30 pm cut-off time will be considered no-shows, and their manuscripts may not be published. Poster authors should accompany their posters from 5:30 to 7:00 pm to answer questions from attendees. All posters and other materials must be removed no later than 7:30 pm. Any posters or materials left behind at the close of the Poster Session will be considered unwanted and will be discarded. SPIE assumes no responsibility for posters left up after the end of each Poster Session.
Panoptic segmentation of wounds in a pig model
Paper 11597-64
Author(s): Thomas E. Tavolara, Adam M. Jorgensen, Metin N. Gurcan, Sean V. Murphy, Muhammad Khalid Khan Niazi, Wake Forest School of Medicine (United States)
Show Abstract
Tooth recognition and classification using multi-task learning and post-processing in dental panoramic radiographs
Paper 11597-65
Author(s): Takumi Morishita, Gifu Univ. (Japan); Chisako Muramatsu, Shiga Univ. (Japan); Xiangrong Zhou, Gifu Univ. (Japan); Ryo Takahashi, Tatsuro Hayashi, Media Co., Ltd. (Japan); Wataru Nishiyama, Asahi Univ. (Japan); Takeshi Hara, Gifu Univ. (Japan); Yoshiko Ariji, Eiichiro Ariji, Aichi Gakuin Univ. (Japan); Akitoshi Katsumata, Asahi Univ. (Japan); Hiroshi Fujita, Gifu Univ. (Japan)
Show Abstract
An automatic diagnosis of Idiopathic Pulmonary Fibrosis (IPF) using domain knowledge-guided attention models in HRCT images
Paper 11597-67
Author(s): Wenxi Yu, Hua Zhou, Univ. of California, Los Angeles (United States); Youngwon Choi, Seoul National Univ. (Korea, Republic of); Jonathan G. Goldin, Pangyu Teng, Grace Hyun J. Kim, Univ. of California, Los Angeles (United States)
Show Abstract
Extremely imbalanced Subarachnoid Hemorrhage detection based on DenseNet-LSTM network with Class-Balanced Loss and Transfer Learning
Paper 11597-68
Author(s): Zhongyang Lu, Masahiro Oda, Yuichiro Hayashi, Tao Hu, Hayashi Ito, Nagoya Univ. (Japan); Takeyuki Watadani, Osamu Abe, The Univ. of Tokyo (Japan); Masahiro Hashimoto, Masahiro Jinzaki, Keio Univ. (Japan); Kensaku Mori, Nagoya Univ. (Japan)
Show Abstract
Radiomic features predict local failure-free survival in stage III NSCLC adenocarcinoma treated with chemoradiation
Paper 11597-69
Author(s): Jose M. Luna Castaneda, Andrew Barsky, Russell T. Shinohara, Alexandra D. Dreyfuss, Leonid Roshkovan, Michelle Hershman, Babak Haghighi, Bardia Yousefi, Peter B. Noel, Keith A. Cengel, Sharyn Katz, Eric S. Diffenderfer, Despina Kontos, Univ. of Pennsylvania (United States)
Show Abstract
3-D multitask deep neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion
Paper 11597-70
Author(s): Hoang Long Le, Sejong Univ. (Korea, Republic of); Yejin Jeon, Ewha Womans Univ. (Korea, Republic of); Hong Gee Roh, Konkuk Univ. (Korea, Republic of); Hyun Jeong Kim, The Catholic Univ. of Korea (Korea, Republic of); Jin Tae Kwak, Sejong Univ. (Korea, Republic of)
Show Abstract
CoroNet: a deep network architecture for identification of COVID-19 from chest X-ray images
Paper 11597-71
Author(s): Chirag Agarwal, Shahin Khobahi, Dan Schonfeld, Mojtaba Soltanalian, Univ. of Illinois at Chicago (United States)
Show Abstract
Deep learning analysis on raw image data -case study on holographic cell analysis
Paper 11597-72
Author(s): Gal Gozes, Shani Ben Baruch, Noa Rotman-Nativ, Darina Roitshtain, Natan T. Shaked, Hayit Greenspan, Tel Aviv Univ. (Israel)
Show Abstract
Photographic cranial shape analysis using deep learning
Paper 11597-73
Author(s): Mohammad Ali Yektaie, Georgetown Univ. (United States); Zahra Ghasemi, Seyed Hossein Hezaveh, Fereshteh Aalamifar, Reza Seifabadi, Marius G. Linguraru, PediaMetrix (United States)
Show Abstract
A computer-aided diagnosis (CAD) scheme to diagnose COVID-19 infection in chest x-ray images
Paper 11597-74
Author(s): Abolfazl Zargari Khuzani, Najmeh Mashhadi, Univ. of California, Santa Cruz (United States); Morteza Heidari, The Univ. of Oklahoma (United States); Donya Khaledyan, Shahid Beheshti Univ. (Iran, Islamic Republic of)
Show Abstract
Applying a new feature fusion method to classify breast lesions
Paper 11597-75
Author(s): Abolfazl Zargari Khuzani, Najmeh Mashhadi, Univ. of California, Santa Cruz (United States); Morteza Heidari, The Univ. of Oklahoma (United States); Donya Khaledyan, Shahid Beheshti Univ. (Iran, Islamic Republic of)
Show Abstract
A novel feature reduction method to improve performance of machine learning model
Paper 11597-76
Author(s): Seyedehnafiseh Mirniaharikandehei, Morteza Heidari, Gopichandh Danala, Warid Islam, Bin Zheng, The Univ. of Oklahoma (United States)
Show Abstract
Spatial matching of magnified 2D mammography images and specimen radiographs: towards improved characterization of suspicious microcalcifications
Paper 11597-77
Author(s): Noor Nakhaei, Chrysostomos Marasinou, Akinyinka Omigbodun, Nina Capiro, Bo Li, Anne Hoyt, William Hsu, Univ. of California, Los Angeles (United States)
Show Abstract
Multiparametric radiomics for predicting the aggressiveness of papillary thyroid carcinoma using hyperspectral images
Paper 11597-78
Author(s): Ka'Toria Edwards, The Univ. of Texas at Dallas (United States); Martin Halicek, The Univ. of Texas at Dallas (United States), Emory Univ. (United States), Georgia Institute of Technology (United States); James V. Little, Amy Y. Chen, Emory Univ. School of Medicine (United States); Baowei Fei, The Univ. of Texas at Dallas (United States), The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Show Abstract
Deep neural networks trained for segmentation are sensitive to brightness changes: preliminary results
Paper 11597-79
Author(s): Zhe Zhu, Mustafa Bashir, Maciej Mazurowski, Duke Univ. (United States)
Show Abstract
Extraction of lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks
Paper 11597-80
Author(s): Yuichiro Hayashi, Masahiro Oda, Chen Shen, Nagoya Univ. (Japan); Masahiro Hashimoto, Keio Univ. School of Medicine (Japan); Yoshito Otake, Nara Institute of Science and Technology (Japan); Toshiaki Akashi, Juntendo Univ. (Japan); Kensaku Mori, Nagoya Univ. (Japan)
Show Abstract
A general fully automated deep-learning method to detect cardiomegaly in chest x-rays
Paper 11597-81
Author(s): Jose Ferreira-Junior, Instituto do Coração do Hospital das Clínicas, Univ. de São Paulo (Brazil); Diego Cardenas, Ramon Moreno, Marina Rebelo, Jose Krieger, Marco Gutierrez, Instituto do Coração do Hospital das Clínicas (Brazil)
Show Abstract
Early detection of at-risk keratoplasties and prediction of future corneal graft rejection from pre-diagnosis endothelial cell images
Paper 11597-82
Author(s): Naomi Joseph, Case Western Reserve Univ. (United States); Beth Ann Benetz, Harry Menegay, Cornea Image Analysis Reading Ctr., Case Western Reserve Univ. (United States); Silke Oellerich, Lamis Baydoun, Gerrit Melles, Netherlands Institute for Innovative Ocular Surgery (Netherlands); Jonathan Lass, David L. Wilson, Case Western Reserve Univ. (United States)
Show Abstract
A system for fully automated monitoring of lesion evolution over time in multiple sclerosis
Paper 11597-83
Author(s): Sven Kuckertz, Florian Weiler, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany); Britta Matusche, Katholisches Klinikum Bochum gGmbH (Germany); Carsten Lukas, St. Josef-Hospital, Ruhr-Univ. Bochum (Germany); Lothar Spies, jung diagnostics GmbH (Germany); Jan Klein, Stefan Heldmann, Fraunhofer-Institut für Digitale Medizin MEVIS (Germany)
Show Abstract
Multi-task learning of perceptive feature for thyroid malignant probability prediction
Paper 11597-84
Author(s): Zixiong Gao, Sun Yat-Sen Univ. (China); Wuping Mai, Guangdong Second Provincial General Hospital (China); Shuyu Wu, Sun Yat-Sen Univ. (China); Hongmei Liu, Guangdong Second Provincial General Hospital (China); Yao Lu, Sun Yat-Sen Univ. (China)
Show Abstract
Improving retinal images segmentation using styleGAN image augmentation
Paper 11597-85
Author(s): Gal Ofir, Tel Aviv Univ. (Israel)
Show Abstract
Fully automatic bone segmentation through contrast enhanced torso CT datasets
Paper 11597-86
Author(s): Ahmed S. Maklad, Beni-Suef Univ. (Egypt), Taibah Univ. (Saudi Arabia); Hassan Hashem, Taibah Univ. (Saudi Arabia); Mikio Matsuhiro, Hidenobu Suzuki, Yoshiki Kawata, Noboru Niki, Tokushima Univ. (Japan)
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Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks
Paper 11597-87
Author(s): Sanne E. Okel, Fons van der Sommen, Endi Selmanaj, Joost A. van der Putten, Technische Univ. Eindhoven (Netherlands); Maarten R. Struyvenberg, Jacques J. Bergman, Amsterdam UMC (Netherlands); Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)
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Applying a feature reduction and fusion-based classifier method to diagnose COVID-19 infection in chest x-ray images
Paper 11597-88
Author(s): Abolfazl Zargari Khuzani, Najmeh Mashhadi, Univ. of California, Santa Cruz (United States); Morteza Heidari, The Univ. of Oklahoma (United States); Donya Khaledyan, Shahid Beheshti Univ. (Iran, Islamic Republic of)
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Automatic segmentation of the nasal cavity and the ethmoidal sinus for the quantification of nasal septal deviations
Paper 11597-89
Author(s): Cristina Oyarzun Laura, Fraunhofer-Institut für Graphische Datenverarbeitung IGD (Germany), Technische Univ. Darmstadt (Germany); Katrin Hartwig, Alexander Distergoft, Tim Hoffmann, Fraunhofer-Institut für Graphische Datenverarbeitung IGD (Germany); Kathrin Scheckenbach, Melanie Brüsseler, Universitätsklinikum Düsseldorf (Germany); Stefan Wesarg, Fraunhofer-Institut für Graphische Datenverarbeitung IGD (Germany)
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Architectural distortion detection in digital breast tomosynthesis with adaptive perceptive field and adaptive convolution kernel shape
Paper 11597-90
Author(s): Yue Li, Sun Yat-Sen Univ. (China); Zilong He, Nanfang Hospital, Southern Medical Univ. (China); Xiangyuan Ma, Sun Yat-Sen Univ. (China); Weimin Xu, Chanjuan Wen, Hui Zeng, Weixiong Zeng, Zeqi Wu, Genggeng Qin, Weiguo Chen, Nanfang Hospital, Southern Medical Univ. (China); Yao Lu, Sun Yat-Sen Univ. (China)
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Liquid state machine ensemble for the detection and probability mapping of small metastatic brain tumors
Paper 11597-91
Author(s): Andrew M. Elliott, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States); Cole Morgan, Rice Univ. (United States); Carlo C. Torres, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States); Ayman Roslend, Univ. of Illinois (United States); Caroline Chung, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
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Renal parenchyma segmentation in abdominal MR images based on cascaded deep convolutional neural network with signal intensity correction
Paper 11597-92
Author(s): Hyeonjin Kim, Helen Hong, Seoul Women's Univ. (Korea, Republic of); Dae Chul Jung, Yonsei Univ. College of Medicine (Korea, Republic of); Kidon Chang, Yonsei Univ. Wonju College of Medicine (Korea, Republic of); Koon Ho Rha, Yonsei Univ. College of Medicine (Korea, Republic of)
Show Abstract
Identification of suspicious breast regions in digital mammograms without lesion annotations
Paper 11597-93
Author(s): Kadie Clancy, Univ. of Pittsburgh (United States)
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Fluorescence lifetime imaging endomicroscopy based ex-vivo lung cancer prediction using multi-scale concatenated-dilation convolutional neural networks
Paper 11597-94
Author(s): Qiang Wang, James R. Hopgood, The Univ. of Edinburgh (United Kingdom); Marta Vallejo, Heriot-Watt Univ. (United Kingdom)
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Assessing reproducibility in Magnetic Resonance (MR) Radiomics features between Deep-Learning segmented and Expert Manual segmented data and evaluating their diagnostic performance in Pregnant Women with suspected Placenta Accreta Spectrum (PAS)
Paper 11597-95
Author(s): Yin Xi, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); Maysam Shahedi, The Univ. of Texas at Dallas (United States); Quyen N. Do, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); James Dormer, The Univ. of Texas at Dallas (United States); Matthew A. Lewis, Baowei Fei, Catherine Y. Spong, Ananth J. Madhuranthakam, Diane M. Twickler, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
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Deep learning-based lesion segmentation in paediatric epilepsy
Paper 11597-96
Author(s): Azad Aminpour, Mehran Ebrahimi, Univ. of Ontario Institute of Technology (Canada); Elysa Widjaja, The Hospital for Sick Children (SickKids) (Canada)
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Classification of mammographic cases using a new quantitative image feature analysis scheme
Paper 11597-97
Author(s): Abolfazl Zargari Khuzani, Najmeh Mashhadi, Univ. of California, Santa Cruz (United States); Morteza Heidari, The Univ. of Oklahoma (United States); Donya Khaledyan, Shahid Beheshti Univ. (Iran, Islamic Republic of)
Show Abstract
3D U-net for registration of lung nodules in longitudinal CT scans
Paper 11597-98
Author(s): Aryan Ghazipour, Benjamin Veasey, Albert Seow, Amir Amini, Univ. of Louisville (United States)
Show Abstract
Machine-learning based clinical plaque detection using a synthetic plaque lesion model for coronary CTA
Paper 11597-99
Author(s): Nikolas D. Schnellbächer, Philips Research (Germany); Haissam Ragab, Universitätsklinikum Hamburg-Eppendorf (Germany); Hannes Nickisch, Tobias Wissel, Philips Research (Germany); Clemens Spink, Gunnar Lund, Universitätsklinikum Hamburg-Eppendorf (Germany); Michael Grass, Philips Research (Germany)
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Comparison between threshold-based and deep learning-based bone segmentation on whole-body CT images
Paper 11597-100
Author(s): Noémie Moreau, KEOSYS (France); Caroline Rousseau, Institut de Cancérologie de l'Ouest (France); Constance Fourcade, Gianmarco Santini, KEOSYS (France); Ludovic Ferrer, Marie Lacombe, Camille Guillerminet, Pascal Jezequel, Mario Campone, Institut de Cancérologie de l'Ouest (France); Nicolas Normand, Lab. des Sciences du Numérique de Nantes (France); Mathieu Rubeaux, KEOSYS (France)
Show Abstract
How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography
Paper 11597-101
Author(s): Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Kindai Univ. Hospital (Japan); Takahiro Yamada, Mitsutaka Nemoto, Yuichi Kimura, Hisashi Handa, Hisashi Yoshida, Koji Abe, Masahiro Tada, Hitoshi Habe, Takashi Nagaoka, Kindai Univ. (Japan); Yoshiaki Ozaki, Kyoto Prefectural Police Headquarters (Japan); Seiun Nin, Kazunari Ishii, Kindai Univ. (Japan); Yongbum Lee, Niigata Univ. (Japan)
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Development of a radiogenomic biomarker for tumor characterization and prognosis in Non-Small Cell Lung Cancer patients
Paper 11597-102
Author(s): Apurva Singh, Zhuoyang Wang, Sharyn Katz, Bardia Yousefi, Despina Kontos, Univ. of Pennsylvania (United States)
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Lung infection and normal region segmentation from CT volumes of COVID-19 cases
Paper 11597-103
Author(s): Masahiro Oda, Yuichiro Hayashi, Nagoya Univ. (Japan); Yoshito Otake, Nara Institute of Science and Technology (Japan); Masahiro Hashimoto, Keio Univ. School of Medicine (Japan); Toshiaki Akashi, Juntendo Univ. (Japan); Kensaku Mori, Nagoya Univ. (Japan)
Show Abstract
Deep learning-based aggressive progression prediction from CT images of hepatocellular carcinoma
Paper 11597-104
Author(s): Jie Tian, Institute of Automation (China)
Show Abstract
Applying quantitative image markers to predict clinical measures after aneurysmal subarachnoid hemorrhage
Paper 11597-105
Author(s): Gopichandh Danala, The Univ. of Oklahoma (United States); Maryum Shoukat, Ahmer Asif, The Univ. of Oklahoma Health Sciences Ctr. (United States); Morteza Heidari, The Univ. of Oklahoma (United States); Masoom Desai, The Univ. of Oklahoma Health Sciences Ctr. (United States); Bin Zheng, The Univ. of Oklahoma (United States)
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Radiomic texture analysis for the assessment of osteoporosis on low-dose thoracic CT scans
Paper 11597-106
Author(s): Jordan D. Fuhrman, Linnea Kremer, The Univ. of Chicago (United States); Yeqing Zhu, Rowena Yip, Icahn School of Medicine at Mount Sinai (United States); Feng Li, Li Lan, The Univ. of Chicago (United States); Artit Jirapatnakul, Claudia I. Henschke, David F. Yankelevitz, Icahn School of Medicine at Mount Sinai (United States); Maryellen L. Giger, The Univ. of Chicago (United States)
Show Abstract
Triplet network for classification of benign and pre-malignant polyps
Paper 11597-107
Author(s): Roger Fonollà, Maciej Smyl, Fons van der Sommen, Technische Univ. Eindhoven (Netherlands); Ramon-Michel Schreuder, Erik J. Schoon, Catharina Hospital (Netherlands); Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)
Show Abstract
Unsupervised survival prediction model from CT images of patients with COVID-19
Paper 11597-108
Author(s): Tomoki Uemura, Kyushu Institute of Technology (Japan), Massachusetts General Hospital (United States); Janne Näppi, Chinatsu Watari, Massachusetts General Hospital (United States), Harvard Medical School (United States); Tohru Kamiya, Kyushu Institute of Technology (Japan); Hiroyuki Yoshida, Massachusetts General Hospital (United States), Harvard Medical School (United States)
Show Abstract
Session 10: Methodology
Improved segmentation by Adversarial U-Net
Paper 11597-42
Author(s): David Sriker, Dana Cohen, Noa Cahan, Hayit Greenspan, Tel Aviv Univ. (Israel)
Show Abstract
Dense-layer-based YOLO-v3 for detection and localization of colon perforations
Paper 11597-43
Author(s): Kai Jiang, Hayato Itoh, Masahiro Oda, Nagoya Univ. (Japan); Taishi Okumura, Yuichi Mori, Masashi Misawa, Takemasa Hayashi, Shin-Ei Kudo, Showa Univ. Northern Yokohama Hospital (Japan); Kensaku Mori, Nagoya Univ. (Japan)
Show Abstract
Deep attention mask regional Convolutional Neural Network for head-and-neck MRI multi-organ auto-delineation
Paper 11597-44
Author(s): Xianjin Dai, Yang Lei, Tonghe Wang, Jun Zhou, Walter Curran, Tian Liu, Xiaofeng Yang, The Winship Cancer Institute of Emory Univ. (United States)
Show Abstract
Multiview and multiclass image segmentation using deep learning in fetal echocardiography
Paper 11597-45
Author(s): Ken C. L. Wong, IBM Research - Almaden (United States); Elena S. Sinkovskaya, Alfred Z. Abuhamad, Eastern Virginia Medical School (United States); Tanveer Syeda-Mahmood, IBM Research - Almaden (United States)
Show Abstract
Multi-scale view-based convolutional neural network for breast cancer classification in ultrasound images
Paper 11597-46
Author(s): Hui Meng, Qingfeng Li, Xuefeng Liu, Beihang Univ. (China); Yong Wang, National Cancer Ctr. (China); Jianwei Niu, Beihang Univ. (China)
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Session 11: Neuroradiology Including Head and Neck
Differentiation of Parkinson’s disease and non-Parkinsonian olfactory dysfunction with structural MRI data
Paper 11597-47
Author(s): Jie Mei, Cécilia Tremblay, Univ. du Québec à Trois-Rivières (Canada); Nikola Stikov, Polytechnique Montréal (Canada); Christian Desrosiers, Ecole de Technologie Supérieure (Canada); Johannes Frasnelli, Univ. du Québec à Trois-Rivières (Canada), Ctr. de Recherche de l'Hôpital du Sacré-Coeur de Montréal (Canada)
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Use of biplane quantitative angiographic imaging with ensemble neural networks to assess reperfusion status during mechanical thrombectomy
Paper 11597-48
Author(s): Mohammad Mahdi Shiraz Bhurwani, Kenneth V. Snyder, Muhammad Waqas, Canon Stroke and Vascular Research Ctr., Univ. at Buffalo (United States); Maxim Mokin, Univ. of South Florida (United States); Ryan A. Rava, Alexander R. Podgorsak, Kelsey N. Sommer, Jason M. Davies, Elad I. Levy, Adnan H. Siddiqui, Ciprian N. Ionita, Canon Stroke and Vascular Research Ctr., Univ. at Buffalo (United States)
Show Abstract
Classification of schizophrenia from functional MRI using large-scale extended Granger causality
Paper 11597-49
Author(s): Axel Wismüller, Univ. of Rochester Medical Ctr. (United States); M. Ali Vosoughi, Univ. of Rochester (United States)
Show Abstract
Otoscopy video screening with deep anomaly detection
Paper 11597-50
Author(s): Weiyao Wang, Johns Hopkins Univ. (United States); Aniruddha Tamhane, The Johns Hopkins Univ. School of Medicine (United States); John Rzasa, Univ. of Maryland, College Park (United States); James Clark, Therese Canares, The Johns Hopkins Univ. School of Medicine (United States); Mathias Unberath, Univ. of Maryland, College Park (United States)
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Fully automated segmentation of brain tumor from multiparametric MRI using 3D context U-Net with deep supervision
Paper 11597-51
Author(s): Mingquan Lin, Boran Zhou, The Winship Cancer Institute of Emory Univ. (United States); Katherine Tang, Duke Univ. (United States); Walter Curran, Tian Liu, Xiaofeng Yang, The Winship Cancer Institute of Emory Univ. (United States)
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Quantitative assessment of deformational plagiocephaly and brachycephaly at the point-of-care
Paper 11597-52
Author(s): Reza Seifabadi, Fereshteh Aalamifar, Seyed Hossein Hezaveh, Marius G. Linguraru, PediaMetrix (United States)
Show Abstract
Lunch Break 12:10 PM - 1:20 PM PST
Session 12: Abdomen II
Multi-institutional observer performance study for bladder cancer treatment response assessment in CT urography with and without computerized decision support
Paper 11597-53
Author(s): Lubomir M. Hadjiiski, Michigan Medicine (United States); Monika Joshi, The Pennsylvania State Univ. (United States); Ajjai Alva, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Michigan Medicine (United States); Galina Kirova-Nedyalkova, Acibadem City Clinic Tokuda Hospital (Bulgaria); Matthew S. Davenport, Prasad R. Shankar, Isaac R. Francis, Kenny H. Cha, Ravi K. Samala, Phillip L. Palmbos, Alon Z. Weizer, Michigan Medicine (United States)
Show Abstract
MRI-based prostate and dominant lesion segmentation using Deep Neural Network
Paper 11597-54
Author(s): Tonghe Wang, Yang Lei, Olayinka Abiodun Ojo, Oladunni Akin-Akintayo, Akinyemi Akintayo, Walter Curran, Tian Liu, David Schuster, Xiaofeng Yang, The Winship Cancer Institute of Emory Univ. (United States)
Show Abstract
Clinically significant prostate cancer detection on multiparametric MRI with self-supervised learning using image context restoration
Paper 11597-55
Author(s): Amir Bolous, Georgia Institute of Technology (United States); Arun Seetharaman, Stanford Univ. (United States); Indrani Bhattacharya, Richard E. Fan, Stanford Univ. School of Medicine (United States); Simon John Christoph Soerensen, Stanford Univ. School of Medicine (United States), Aarhus Univ. (Denmark); Leo C. Chen, Pejman Ghanouni, Geoffrey A. Sonn, Mirabela Rusu, Stanford Univ. School of Medicine (United States)
Show Abstract
Intestinal region reconstruction of ileus cases from 3D CT images based on graphical representation and its visualization
Paper 11597-56
Author(s): Hirohisa Oda, Yuichiro Hayashi, Nagoya Univ. (Japan); Takayuki Kitasaka, Aichi Institute of Technology (Japan); Yudai Tamada, 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
Modifying UNet for small dataset: a simplified U-Net version for Liver Parenchyma segmentation
Paper 11597-57
Author(s): Pravda Prasad, Oslo Univ. Hospital (Norway)
Show Abstract
Session 13: Lung III
RV strain classification from 3D CTPA scans using weakly supervised residual attention model
Paper 11597-58
Author(s): Noa Cahan, Tel Aviv Univ. (Israel); Edith M. Marom, Yiftach Barash, Shelly Soffer, Eli Konen, Eyal Klang, The Chaim Sheba Medical Ctr., Tel Hashomer (Israel); Hayit Greenspan, Tel Aviv Univ. (Israel)
Show Abstract
Reinforced learning from serial CT to improve early diagnosis of lung cancer in screening
Paper 11597-59
Author(s): Yifan Wang, Michigan Medicine, Univ. of Michigan (United States); Chuan Zhou, Michigan Medicine (United States); Lei Ying, Univ. of Michigan (United States); Heang-Ping Chan, Lubomir M. Hadjiiski, Aamer Chughtai, Ella A. Kazerooni, Michigan Medicine (United States)
Show Abstract
Overcoming the "catastrophic forgetting" effect in transfer learning to achieve vendor independent performance for the COVID-19 pneumonia classification task using chest x-ray radiographs
Paper 11597-60
Author(s): Ran Zhang, Guang-Hong Chen, Univ. of Wisconsin School of Medicine and Public Health (United States)
Show Abstract
Self-training with improved regularization for sample-efficient chest X-Ray classification
Paper 11597-61
Author(s): Deepta Rajan, IBM Research - Almaden (United States); Jayaraman J. Thiagarajan, Lawrence Livermore National Lab. (United States); Alexandros Karargyris, Satyananda Kashyap, IBM Research - Almaden (United States)
Show Abstract
Severity assessment of COVID-19 using imaging descriptors: a deep-learning transfer learning approach from non-COVID-19 pneumonia
Paper 11597-62
Author(s): Ravi K. Samala, Lubomir Hadjiiski, Heang-Ping Chan, Jadranka Stojanovska, Prachi Agarwal, Christopher Fung, Michigan Medicine (United States)
Show Abstract
COVID-19 opacity segmentation in chest CT via HydraNet: a joint learning multi-decoder network
Paper 11597-63
Author(s): Nimrod Sagie, Hayit Greenspan, Shiri Almog, Ayelet Talby, Tel Aviv Univ. (Israel)
Show Abstract
Conference Committee
Conference Chairs
Program Committee
Program Committee continued...
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