Town and Country Resort & Convention Center
San Diego, California, United States
16 - 21 February 2019
Conference 10950
Computer-Aided Diagnosis
Sunday - Wednesday 17 - 20 February 2019
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Program Committee continued...
Sunday 17 February Show All Abstracts
Session 1:
Breast I
Sunday 17 February 2019
8:00 AM - 9:40 AM
Vendor-independent soft tissue lesion detection using weakly supervised and unsupervised adversarial domain adaptation
Paper 10950-1
Author(s): Joris van Vugt, Elena Marchiori, Radboud Univ. Nijmegen (Netherlands); Ritse Mann, Radboud Univ. Medical Ctr. (Netherlands); Albert Gubern-Mérida, ScreenPoint Medical (Netherlands); Nikita Moriakov, Jonas Teuwen, Radboud Univ. Medical Ctr. (Netherlands)
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Detecting mammographically-occult cancer in women with dense breasts using deep convolutional neural network and Radon cumulative distribution transform
Paper 10950-2
Author(s): Juhun Lee, Robert M. Nishikawa, Univ. of Pittsburgh (United States)
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Reducing overfitting of a deep learning breast mass detection algorithm in mammography using synthetic images
Paper 10950-3
Author(s): Kenny H. Cha, Nicholas Petrick, Aria Pezeshk, Christian G. Graff, Diksha Sharma, Andreu Badal, Aldo Badano, Berkman Sahiner, U.S. Food and Drug Administration (United States)
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Deep learning for identifying breast cancer malignancy and false recalls: a robustness study
Paper 10950-4
Author(s): Kadie Clancy, Lei Zhang, Aly A. Mohamed, Sarah Aboutalib, Wendie Berg, Shandong Wu, Univ. of Pittsburgh (United States)
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Evaluating deep learning techniques for dynamic contrast-enhanced MRI in the diagnosis of breast cancer
Paper 10950-5
Author(s): Rachel Anderson, Hui Li, The Univ. of Chicago Medicine (United States); Yu Ji, The Univ. of Chicago Medicine (United States), Tianjin Medical Univ. Cancer Institute & Hospital (China); Peifang Liu, Tianjin Medical Univ. Cancer Institute & Hospital (China); Maryellen L. Giger, The Univ. of Chicago Medicine (United States)
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Session 2:
Brain
Sunday 17 February 2019
10:10 AM - 12:10 PM
Registration based detection and quantification of intracranial aneurysm growth
Paper 10950-6
Author(s): Ziga Bizjak, Tim Jerman, Boštjan Likar, Franjo Pernuš, Univ. of Ljubljana (Slovenia); Aichi Chien, Univ. of California, Los Angeles (United States); Ziga Spiclin, Univ. of Ljubljana (Slovenia)
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Reliability of computer-aided diagnosis tools with multicenter MR datasets: impact of training protocol
Paper 10950-7
Author(s): Mariana P. Bento , Roberto M. Souza, Marina Salluzzi, Richard Frayne, Univ. of Calgary (Canada)
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Automatic multi-modality segmentation of gross tumor volume for head and neck cancer radiotherapy using 3D U-Net
Paper 10950-8
Author(s): Zhe Guo, Massachusetts General Hospital (United States), Beijing Institute of Technology (China); Ning Guo, Kuang Gong, Quanzheng Li, Massachusetts General Hospital (United States)
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Automatic strategy for extraction of anthropometric measurements for the diagnostic and evaluation of deformational plagiocephaly from infant’s head models
Paper 10950-9
Author(s): Bruno Oliveira, Helena R. Torres, Pedro Morais, Instituto Politécnico do Cávado e do Ave (Portugal); Fernando Veloso, Instituto Politécnico do Cávado e do Ave (Portugal); Estela Vilhena, João L. Vilaça, Instituto Politécnico do Cávado e do Ave (Portugal)
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Radiomics of the lesion habitat on pre-treatment MRI to predict response to chemo-radiation therapy in Glioblastoma
Paper 10950-10
Author(s): Ruchika Verma, Ramon Correa, Virginia Hil, Niha G. Beig, Abdelkar Mahammedi, Anant Madabhushi, Pallavi Tiwari, Case Western Reserve Univ. (United States)
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Modeling normal brain asymmetry in MR images applied to anomaly detection without segmentation and data annotation
Paper 10950-11
Author(s): Samuel B. Martins, Barbara C. Benato, Bruna F. Silva, Clarissa L. Yasuda, Alexandre X. Falcão, UNICAMP (Brazil)
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Lunch Break 12:10 PM - 1:20 PM
Session 3:
Breast II
Sunday 17 February 2019
1:20 PM - 3:00 PM
Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing
Paper 10950-12
Author(s): Bas H. van der Velden, Bob D. de Vos, Univ. Medical Ctr. Utrecht (Netherlands); Claudette E. Loo, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital (Netherlands); Hugo J. Kuijf, Ivana Išgum, Kenneth G. A. Gilhuijs, Univ. Medical Ctr. Utrecht (Netherlands)
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Multiview mammographic mass detection based on a single shot detection system
Paper 10950-13
Author(s): Yinhao Ren, Rui Hou, Carl E. Ravin Advanced Imaging Labs. (United States), Duke Univ. (United States); Dehan Kong, Beijing Institute of Technology (China); Yue Geng, Tsinghua Univ. (China); Lars J. Grimm, Jeffrey R. Marks, Duke Univ. School of Medicine (United States); Joseph Y. Lo, Carl E. Ravin Advanced Imaging Labs. (United States)
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A deep learning method for volumetric breast density estimation from processed full field digital mammograms
Paper 10950-14
Author(s): Doiriel Vanegas Camargo, Mahlet Birhanu, Univ. de Girona (Spain); Nico Karssemeijer, Albert Gubern-Mérida, Michiel Kallenberg, ScreenPoint Medical (Netherlands)
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Breast density follow-up decision support system using deep convolutional models
Paper 10950-15
Author(s): Sun Young Park, Dustin Sargent, IBM Watson Health (United States)
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DCE-MRI based analysis of intratumor heterogeneity by decomposing method for prediction of HER2 status in breast cancer
Paper 10950-16
Author(s): Peng Zhang, Ming Fan, Yuanzhe Li, Hangzhou Dianzi Univ. (China); Maosheng Xu, Zhejiang Provincial Hospital of TCM (China); Lihua Li, Hangzhou Dianzi Univ. (China)
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Session 4:
Breast III and Heart
Sunday 17 February 2019
3:30 PM - 5:30 PM
Association of computer-aided detection results and breast cancer risk
Paper 10950-17
Author(s): Seyedeh-Nafiseh Mirnia-harikandehei, Morteza Heidari, Gopichandh Danala, The Univ. of Oklahoma (United States); Wei Qian, The Univ. of Texas at El Paso (United States); Yuchen Qiu, Bin Zheng, The Univ. of Oklahoma (United States)
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Breast parenchyma analysis and classification for breast masses detection using texture feature descriptors and neural networks in dedicated breast CT images
Paper 10950-18
Author(s): Marco Caballo, Jonas Teuwen, Ritse Mann, Ioannis Sechopoulos, Radboud Univ. Medical Ctr. (Netherlands)
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Visual evidence for interpreting diagnostic decision of deep neural network in computer-aided diagnosis
Paper 10950-19
Author(s): Seong Tae Kim, Jae-Hyeok Lee, Yong Man Ro, KAIST (Korea, Republic of)
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Automated measurement of fetal right-myocardial performance index from pulsed wave Doppler spectrum
Paper 10950-20
Author(s): Rahul Suresh, Srinivasan Sivanandan, Nitin Singhal, Samsung R&D Institute India - Bangalore (India); Jinyong Lee, SAMSUNG Medison Co., Ltd. (Korea, Republic of); Mi-Young Lee, Hye-Sung Won, Asan Medical Ctr. (Korea, Republic of)
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U-Net inspired architecture ensembles for left atrial segmentation
Paper 10950-21
Author(s): Christopher Wang, Carleton Univ. (Canada); Eranga Ukwatta, Carleton Univ. (Canada), Univ. of Guelph (Canada); Martin Rajchl, Imperial College London (United Kingdom)
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A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography
Paper 10950-22
Author(s): Lambros S. Athanasiou, Institute for Medical Engineering & Science (United States), Brigham and Women's Hospital (United States); Max L. Olender, Institute for Medical Engineering & Science (United States); José M. de la Torre Hernandez, Unidad de Cardiologia Intervencionista (Spain); Eyal Ben-Assa, Massachusetts General Hospital, Harvard Medical School (United States); Elazer R. Edelman, Institute for Medical Engineering & Science (United States), Brigham and Women's Hospital (United States)
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Monday 18 February Show All Abstracts
Session 5:
Lung I
Monday 18 February 2019
8:00 AM - 9:40 AM
PHT-bot: a deep learning based system for automatic risk stratification of COPD patients based upon signs of pulmonary hypertension
Paper 10950-23
Author(s): David Chettrit, Orna Bregman Amitai, Amir Bar, Zebra Medical Vision, Inc. (Israel); Itamar Alfred Tamir, Clalit Health Services (Israel); Eldad Elnekave, Zebra Medical Vision, Inc. (Israel)
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Identifying disease-free chest x-ray images with deep transfer learning
Paper 10950-24
Author(s): Ken C. L. Wong, Mehdi Moradi, Joy T. Wu, Tanveer Syeda-Mahmood, IBM Research - Almaden (United States)
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Analysis of deep convolutional features for detection of lung nodules in computed tomography
Paper 10950-25
Author(s): Ravi K. Samala, Heang-Ping Chan, Caleb Richter, Lubomir M. Hadjiiski, Chuan Zhou, Jun Wei, Univ. of Michigan (United States)
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A combination of intra- and peritumoral features on baseline CT scans is associated with overall survival in non-small cell lung cancer patients treated with nivolumab
Paper 10950-26
Author(s): Mohammadhadi Khorrami, Mehdi Alilou, Prateek Prasanna, Pingfu Fu, Case Western Reserve Univ. (United States); Vamsidhar Velcheti, Cleveland Clinic (United States); Anant Madabhushi, Case Western Reserve Univ. (United States)
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Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs
Paper 10950-27
Author(s): Sivaramakrishnan Rajaraman, U.S. National Library of Medicine (United States); Sema Candemir, The Ohio State University Wexner Medical Center (United States); George Thoma, Sameer Antani, U.S. National Library of Medicine (United States)
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Session 6:
Abdomen
Monday 18 February 2019
10:10 AM - 12:10 PM
Artifact-driven sampling schemes for robust female pelvis CBCT segmentation using deep learning
Paper 10950-28
Author(s): Annika Hänsch, Volker Dicken, Jan Klein, Fraunhofer MEVIS (Germany); Tomasz Morgas, Varian Medical Systems, Inc. (United States); Benjamin Haas, Varian Medical Systems Imaging Lab. GmbH (Switzerland); Horst K. Hahn, Fraunhofer MEVIS (Germany)
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A probabilistic approach for interpretable deep learning in the diagnosis of liver lesions
Paper 10950-29
Author(s): Clinton J. Wang, Yale School of Medicine (United States); Charlie A. Hamm, Yale School of Medicine (United States), Charité Universitätsmedizin Berlin (Germany); Brian S. Letzen, Yale School of Medicine (United States); James S. Duncan, Yale School of Medicine (United States), Yale Univ. (United States)
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Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports
Paper 10950-30
Author(s): Matias Benitez, Duke Clinical Research Institute (United States); James Tian, Mark Kelly, Vignesh Selvakumaran, Duke Univ. School of Medicine (United States); Matthew Phelan, Duke Clinical Research Institute (United States); Maciej A. Mazurowski, Duke Univ. (United States); Joseph Y. Lo, Geoffrey Rubin, Duke Univ. School of Medicine (United States); Ricardo Henao, Duke Clinical Research Institute (United States)
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Bladder cancer staging in CT urography: estimation and validation of decision thresholds for a radiomics-based decision support system
Paper 10950-31
Author(s): Dhanuj Gandikota, Lubomir M. Hadjiiski, Heang-Ping Chan, Univ. of Michigan (United States); Kenny H. Cha, U.S. Food and Drug Administration (United States); Ravi K. Samala, Elaine M. Caoili, Richard H. Cohan, Alon Z. Weizer, Ajjai Alva, Chintana Paramagul, Jun Wei, Chuan Zhou, Univ. of Michigan (United States)
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Automatic MR kidney segmentation for autosomal dominant polycystic kidney disease
Paper 10950-32
Author(s): Guangrui Mu, Southern Medical Univ. (China), United Imaging (China); Yiyi Ma, Shanghai Changzheng Hospital (China); Miaofei Han, Yiqiang Zhan, Xiang Zhou, Yaozong Gao, United Imaging (China)
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2D and 3D bladder segmentation using U-Net-based deep-learning
Paper 10950-33
Author(s): Xiangyuan Ma, Sun Yat-Sen Univ. (China), Univ. of Michigan (United States); Lubomir M. Hadjiiski, Jun Wei, Heang-Ping Chan, Univ. of Michigan (United States); Kenny H. Cha, U.S. Food and Drug Administration (United States); Richard H. Cohan, Elaine M. Caoili, Ravi K. Samala, Chuan Zhou, Univ. of Michigan (United States); Yao Lu, Sun Yat-Sen Univ. (China)
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Lunch Break 12:10 PM - 1:20 PM
Session 7:
Multiorgan and Colon
Monday 18 February 2019
1:20 PM - 3:40 PM
Automatic anatomy partitioning of the torso region on CT images by using a deep convolutional network with a majority voting
Paper 10950-34
Author(s): Xiangrong Zhou, Gifu Univ. School of Medicine (Japan); Takuya Kojima, Gifu Univ. (Japan); Song Wang, Univ. of South Carolina (United States); Xinxin Zhou, Nagoya Bunri Univ. (Japan); Takeshi Hara, Gifu Univ. (Japan); Taiki Nozaki, St. Luke’s International Hospital (Japan); Masaki Matsusako, St. Luke's International Hospital (Japan); Hiroshi Fujita, Gifu Univ. (Japan)
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Automatic multi-organ segmentation in thorax CT images using U-Net-GAN
Paper 10950-35
Author(s): Yang Lei, Yingzi Liu, Sibo Tian, Xiaojun Jiang, Kristin Higgins, Jonathan J. Beitler, David S. Yu, Tian Liu, Walter J. Curran, Emory Univ. (United States); Yi Fang, New York Univ. (United States); Xiaofeng Yang, Emory Univ. (United States)
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Polyp segmentation and classification using predicted depth from monocular endoscopy
Paper 10950-36
Author(s): Faisal Mahmood, Ziyun Yang, Richard Chen, Daniel Borders, Wenhao Xu, Nicholas J. Durr, Johns Hopkins Univ. (United States)
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Computer-aided classification of colorectal polyps using blue-light and linked-color imaging
Paper 10950-37
Author(s): Thom Scheeve, Technische Univ. Eindhoven (Netherlands); Ramon-Michel Schreuder, Catharina Hospital (Netherlands); Fons van der Sommen, Technische Univ. Eindhoven (Netherlands); Joep E. G. IJspeert, Evelien Dekker, Amsterdam UMC (Netherlands), Univ. van Amsterdam (Netherlands); Erik J. Schoon, Catharina Hospital (Netherlands); Peter H. N. De With, Technische Univ. Eindhoven (Netherlands)
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Ensemble 3D residual network (E3D-ResNet) for reduction of false-positive polyp detections in CT colonography
Paper 10950-38
Author(s): Tomoki Uemura, Kyushu Institute of Technology (Japan), Massachusetts General Hospital (United States), Harvard Medical School (United States); Janne J. Näppi, Massachusetts General Hospital (United States), Harvard Medical School (United States); Huimin Lu, Hyoungseop Kim, Kyushu Institute of Technology (Japan); Rie Tachibana, National Institute of Technology, Oshima College (Japan); Toru Hironaka, Hiroyuki Yoshida, Massachusetts General Hospital (United States), Harvard Medical School (United States)
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A local geometrical metric-based model for polyp classification
Paper 10950-39
Author(s): Weiguo Cao, Marc J. Pomeroy, Zhengrong Liang, The State Univ. of New York (United States)
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Polyp-size classification with RGB-D features for colonoscopy
Paper 10950-40
Author(s): Hayato Itoh, Holger Roth, Nagoya Univ. (Japan); Yuichi Mori, Masashi Misawa, Showa Univ. Northern Yokohama Hospital (Japan); Masahiro Oda, Nagoya Univ. (Japan); Shin-Ei Kudo, Showa Univ. Northern Yokohama Hospital (Japan); Kensaku Mori, Nagoya Univ. (Japan)
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Plenary and Awards Session
Monday 18 February 2019
4:10 PM - 5:30 PM
Tuesday 19 February Show All Abstracts
Session 8:
Lung II
Tuesday 19 February 2019
8:00 AM - 9:40 AM
Handling label noise through model confidence and uncertainty: application to chest radiograph classification
Paper 10950-41
Author(s): Erdi Calli, Diagnostic Image Analysis Group (Netherlands)
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Classification of chest CT using case-level weak supervision
Paper 10950-42
Author(s): Ruixiang Tang, Songyue Han, Rui Hou, Geoffrey D. Rubin, Joseph Y. Lo, Duke Univ. (United States)
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Deep adversarial one-class learning for normal and abnormal chest radiograph classification
Paper 10950-43
Author(s): Yuxing Tang, National Institutes of Health Clinical Ctr. (United States); Youbao Tang, National Institutes of Health (United States); Mei Han, Ping An Technology, US Research Lab (United States); Jing Xiao, Ping An Technology Co., Ltd. (China); Ronald M. Summers, National Institutes of Health (United States)
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Image biomarkers for quantitative analysis of idiopathic interstitial pneumonia
Paper 10950-44
Author(s): Young-Wouk Kim, Télécom SudParis (France), Avicenne Hospital (France); Sebastián Roberto Tarando, Télécom SudParis (France); Pierre-Yves Brillet, Univ. Paris 13 (France), Avicenne Hospital (France); Catalin Fetita, Télécom SudParis (France)
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Patient-specific outcome simulation after surgical correction of pectus excavatum: a preliminary study
Paper 10950-45
Author(s): Mafalda Couto, João Gomes-Fonseca, Instituto de Investigação em Ciências da Vida e da Saúde, Univ. do Minho (Portugal); Tiago Henriques-Coelho, Univ. do Porto (Portugal); Jaime C. Fonseca, António C. M. Pinho, Univ. do Minho (Portugal); Jorge Correia-Pinto, Instituto de Investigação em Ciências da Vida e da Saúde, Univ. do Minho (Portugal); João L. Vilaça, Instituto Politécnico do Cávado e do Ave (Portugal)
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Session 9:
Radiomics I
Tuesday 19 February 2019
10:10 AM - 12:10 PM
Effect of diversity of patient population and acquisition systems on the use of radiomics and machine learning for classification of 2,861 breast lesions
Paper 10950-46
Author(s): Heather M. Whitney, Wheaton College (United States), The Univ. of Chicago (United States); Yu Ji, Tianjin Medical Univ. Cancer Institute & Hospital (China), The Univ. of Chicago (United States); Hui Li, Alexandra Edwards, John Papaioannou, The Univ. of Chicago (United States); Peifang Liu, Tianjin Medical Univ. Cancer Institute & Hospital (China); Maryellen L. Giger, The Univ. of Chicago (United States)
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Radiogenomic characterization of response to chemo-radiation therapy in Glioblastoma is associated with PI3K/AKT/mTOR and apoptosis signaling pathways
Paper 10950-47
Author(s): Niha G. Beig, Prateek Prasanna, Case Western Reserve Univ. (United States); Virginia Hill, Northwestern Univ. (United States); Ruchika Verma, Vinay Varadan, Anant Madabhushi, Pallavi Tiwari, Case Western Reserve Univ. (United States)
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Identifying optimal input using multilevel radiomics for predicting pulmonary function in lung cancer patients treated with radiotherapy
Paper 10950-48
Author(s): Sang Ho Lee, Peijin Han, Russell K. Hales, Khinh R. Voong, Todd R. McNutt, Junghoon Lee, Johns Hopkins Univ. School of Medicine (United States)
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Texture-based prostate cancer classification on MRI: how does inter-class size mismatch affect measured system performance?
Paper 10950-49
Author(s): Ryan Alfano, Derek Soetemans, Western Univ. (Canada); Glenn Bauman, London Health Sciences Ctr. (Canada); Mena Gaed, Western Univ. (Canada); Madeleine Moussa, José Gomez-Lemus, Joseph Chin, London Health Sciences Ctr. (Canada); Stephen Pautler, St. Joseph's Health Ctr. (Canada); Aaron Ward, Western Univ. (Canada)
Show Abstract
Deformation heterogeneity radiomics to predict molecular subtypes of pediatric Medulloblastoma on routine MRI
Paper 10950-50
Author(s): Sukanya Raj Iyer, Marwa Ismail, Ramon Correa, Prateek Prasanna, Niha G. Beig, Kaustav Bera, Case Western Reserve Univ. (United States); Volodymyr Statsevych, Cleveland Clinic (United States); Benita Tamrazi, Ashley Margol, Alexander Judkins, Children's Hospital Los Angeles (United States); Anant Madabhushi, Pallavi Tiwari, Case Western Reserve Univ. (United States)
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Quantitative vessel tortuosity radiomics on non-contrast lung CT predict response to immunotherapy, overall survival and are associated with PD-L1 expression
Paper 10950-51
Author(s): Mehdi Alilou, Pranjal Vaidya, Case Western Reserve Univ. (United States); Alexia Zagouras, Pradnya Patil, Cleveland Clinic (United States); Mohammadhadi Khorrami, Case Western Reserve Univ. (United States); Pingfu Fu, Vamsidhar Velcheti, Cleveland Clinic (United States); Anant Madabhushi, Case Western Reserve Univ. (United States)
Show Abstract
Tuesday/Wednesday Poster Viewing
Tuesday 19 February 2019
12:00 PM - 9:00 PM

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
Session 10:
Keynote Session
Tuesday 19 February 2019
1:20 PM - 3:00 PM

Keynote presentation to be followed by a 40 minute panel discussion on deep learning in CAD.
The U-net and its impact to medical imaging (Keynote Presentation)
Paper 10950-52
Author(s): Bernardino Romera-Paredes, Google DeepMind (United Kingdom)
Show Abstract
Session 11:
Lung III
Tuesday 19 February 2019
3:30 PM - 4:50 PM
Weakly-supervised deep learning of interstitial lung disease types on CT images
Paper 10950-53
Author(s): Chenglong Wang, Takayasu Moriya, Yuichiro Hayashi, Holger Roth, Nagoya Univ. (Japan); Le Lu, NVIDIA Corp. (United States); Masahiro Oda, Nagoya Univ. (Japan); Hirotugu Ohkubo, Nagoya City Univ. (Japan); Kensaku Mori, Nagoya Univ. (Japan)
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Efficient learning in computer-aided diagnosis through label propagation
Paper 10950-54
Author(s): Samuel Berglin, Univ. of Wisconsin-Madison (United States); Eura Shin, Univ. of Kentucky (United States); Daniela Raicu, Jacob Furst, DePaul Univ. (United States)
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Computer-aided CT image features improving the malignant risk prediction in pulmonary nodules suspicious for lung cancer
Paper 10950-55
Author(s): Yoshiki Kawata, Takeru Kageyama, Noboru Niki, Tokushima Univ. (Japan); Masahiko Kusumoto, National Cancer Ctr. Hospital East (Japan); Hironobu Ohmatsu, Medical Affairs Sec, Abashiri Prisontion (Japan); Keiju Aokage, Genichiro Ishii, Yuji Matsumoto, Takaaki Tsuchida, National Cancer Ctr. Hospital East (Japan); Kenji Eguchi, Teikyo Univ. (Japan); Masahiro Kaneko, Tokyo Health Service Association (Japan)
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Augmenting LIDC dataset using 3D generative adversarial networks to improve lung nodule detection
Paper 10950-56
Author(s): Chufan Gao, Purdue Univ. (United States); Stephen Clark, The Univ. of Tennessee at Chattanooga (United States)
Show Abstract
Session WK3:
WORKSHOP: Live Demonstrations
Tuesday 19 February 2019
5:00 PM - 7:00 PM
Session Chairs:
Horst K. Hahn, Fraunhofer MEVIS (Germany) ;
Lubomir M. Hadjiiski, Univ. of Michigan Health System (United States)

See Special Events for more information.
Wednesday 20 February Show All Abstracts
Session 12:
Vascular and Radiomics II
Wednesday 20 February 2019
8:00 AM - 9:40 AM
A semi-supervised CNN learning method with pseudo-class labels for vascular calcification detection on low dose CT scans
Paper 10950-57
Author(s): Jiamin Liu, Jianhua Yao, Mohammadhadi Bagheri, Ronald M. Summers, National Institutes of Health (United States)
Show Abstract
Variability in radiomics features among iDose reconstruction levels
Paper 10950-58
Author(s): Joseph J. Foy, The Univ. of Chicago (United States); Mena Shenouda, Univ. of Michigan (United States); Sahar Ramahi, Univ. of Illinois (United States); Samuel G. Armato, Daniel Ginat, The Univ. of Chicago (United States)
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Development and validation of a radiomics-based method for macrovascular invasion prediction in hepatocellular carcinoma with prognostic implication
Paper 10950-59
Author(s): Jingwei Wei, Institute of Automation (China); Sirui Fu, Zhuhai People’s Hospital (China); Shauitong Zhang, Institute of Automation (China); Jie Zhang, Zhuhai People’s Hospital (China); Dongsheng Gu, Institute of Automation (China); Xiaoqun Li, Zhongshan City People's Hospital (China); Xudong Chen, Shenzhen People’s Hospital (China); Xiaofeng He, Nanfang Hospital of the Southern Medical Univ. (China); Jianfeng Yan, Yangjiang People’s Hospital (China); Ligong Lu, Ctr. of Intervention Radiology (China); Jie Tian, Institute of Automation (China)
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A combination of intra- and peri-tumoral radiomic features from MRI predict prostate cancer risk: a multi-site study
Paper 10950-60
Author(s): Ahmad Algohary, Case Western Reserve Univ. (United States)
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Efficient detection of vascular structures using locally connected filtering
Paper 10950-61
Author(s): Amele Florence Kouvahe, Catalin Fetita, Télécom SudParis (France)
Show Abstract
Session 13:
Eyes and New Approaches
Wednesday 20 February 2019
10:10 AM - 12:10 PM
Deep learning for automated screening and semantic segmentation of age-related and juvenile atrophic macular degeneration
Paper 10950-62
Author(s): Ziyuan Wang, SriniVas R. Sadda, Zhihong Hu, Doheny Eye Institute (United States)
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Improved interpretability for computer-aided severity assessment of retinopathy of prematurity
Paper 10950-63
Author(s): Mara Graziani, HES-SO Valais-Wallis, Haute Ecole Spécialisée de Suisse Occidentale (Switzerland); James M. Brown, Athinoula A. Martinos Ctr. for Biomedical Imaging (United States); Vincent Andrearczyk, Haute Ecole Spécialisée de Suisse Occidentale (Switzerland); Veysi Yildiz, Northeastern Univ. (United States); J. Peter Campbell, Oregon Health & Science Univ. (United States); Deniz Erdogmus, Stratis Ioannidis, Northeastern Univ. (United States); Michael F. Chiang, Casey Eye Institute, Oregon Health & Science Univ. (United States); Jayashree Kalpathy-Cramer, Athinoula A. Martinos Ctr. for Biomedical Imaging (United States); Henning Müller, Haute Ecole Spécialisée de Suisse Occidentale (Switzerland)
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Reproducibility of CT-based texture feature quantification of simulated and 3D-printed trabecular bone: influence of noise and reconstruction kernel
Paper 10950-64
Author(s): Qin Li, U.S. Food and Drug Administration (United States)
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Fusing attributes predicted via conditional GANs for improved skin lesion classification
Paper 10950-65
Author(s): Faisal Mahmood, Jeremiah Johnson, Ziyun Yang, Nicholas J. Durr, Johns Hopkins Univ. (United States)
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Age prediction using a large chest x-ray dataset
Paper 10950-66
Author(s): Alexandros Karargyris, Satyananda Kashyap, Joy T. Wu, Arjun Sharma, Mehdi Moradi, Tanveer Syeda-Mahmood, IBM Research - Almaden (United States)
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Using multi-task learning to improve diagnostic performance of convolutional neural networks
Paper 10950-67
Author(s): Mengjie Fang, Di Dong, Institute of Automation (China), Univ. of Chinese Academy of Sciences (China); Ruijia Sun, Beijing Cancer Hospital (China); Li Fan, Changzheng Hospital, Second Military Medical Univ. (China); Yingshi Sun, Beijing Cancer Hospital (China); Shiyuan Liu, Changzheng Hospital, Second Military Medical Univ. (China); Jie Tian, Institute of Automation (China), Univ. of Chinese Academy of Sciences (China)
Show Abstract
Lunch Break 12:10 PM - 1:20 PM
Session 14:
Radiomics III and Oncology
Wednesday 20 February 2019
1:20 PM - 3:00 PM
Stability of radiomic features of liver lesions from manual delineation in CT scans
Paper 10950-68
Author(s): Jan H. Moltz, Fraunhofer MEVIS (Germany)
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Use of convolutional neural networks to predict risk of masking by mammographic density
Paper 10950-69
Author(s): Theo Cleland, James G. Mainprize, Olivier Alonzo-Proulx, Sunnybrook Research Institute (Canada); Jennifer A. Harvey, Univ. of Virginia Health System (United States); Anne L. Martel, Martin J. Yaffe, Sunnybrook Research Institute (Canada), Univ. of Toronto (Canada)
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A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy
Paper 10950-70
Author(s): Thom Scheeve, Technische Univ. Eindhoven (Netherlands); Maarten R. Struyvenberg, Amsterdam UMC (Netherlands), Univ. van Amsterdam (Netherlands); Wouter L. Curvers, Catharina Hospital (Netherlands), Amsterdam UMC (Netherlands), Univ. van Amsterdam (Netherlands); Albert J. de Groof, Amsterdam UMC (Netherlands), Univ. van Amsterdam (Netherlands); Erik J. Schoon, Catharina Hospital (Netherlands); Jacques J. G. H. M. Bergman, Amsterdam UMC (Netherlands), Univ. van Amsterdam (Netherlands); Fons van der Sommen, Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)
Show Abstract
Radiomics analysis potentially reduces over-diagnosis of prostate cancer with prostate specific antigen levels of 4-10 ng/ml based on DWI data
Paper 10950-71
Author(s): Jie Tian, Institute of Automation (China)
Show Abstract
Homogenization of breast MRI across imaging centers and feature analysis using unsupervised deep embedding
Paper 10950-72
Author(s): Ravi K. Samala, Heang-Ping Chan, Lubomir M. Hadjiiski, Chintana Paramagul, Mark A. Helvie, Colleen Neal, Univ. of Michigan (United States)
Show Abstract
Award Announcements
Wednesday 20 February 2019
3:00 PM - 3:05 PM

The Computer-Aided Diagnosis conference RFW runners up and poster award recipients will be recognized and certificates distributed.
Session 15:
BreastPathQ: Cancer Cellularity Challenge
Wednesday 20 February 2019
3:30 PM - 5:30 PM

Results from the Cancer Cellularity Challenge will be discussed in this session. All attendees are encouraged to attend, especially those involved with the Computer-Aided Diagnosis and Digital Pathology conferences.
Wednesday Poster Session
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Session PS1:
Posters: Bone
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis
Paper 10950-73
Author(s): Nina Tubau, Priscille de Dumast, Marilia Yatabe, Antonio Ruellas, Marcos Ioshida, Univ. of Michigan (United States); Beatriz Paniagua, Kitware, Inc. (United States); Martin Styner, The Univ. of North Carolina at Chapel Hill (United States); Lucia Cevidanes, Juan C. Prieto, Univ. of Michigan (United States)
Show Abstract
Automatic detection and localization of bone erosion in hand HR-pQCT
Paper 10950-74
Author(s): Jintao Ren, Arash Moaddel H., Univ. of Copenhagen (Denmark); Ellen M. Hauge, Kresten K. Keller, Rasmus K. Jensen, Aarhus Univ. (Denmark); Francois B. Lauze, Univ. of Copenhagen (Denmark)
Show Abstract
Spinal curvature segmentation and location by transfer learning
Paper 10950-75
Author(s): Jiashi Zhao, Zhengang Jiang, Changchun Univ. of Science and Technology (China); Kensaku Mori, Nagoya Univ. (Japan); Liyuan Zhang, Wei He, Weili Shi, Yu Miao, Fei Yan, Fei He, Changchun Univ. of Science and Technology (China)
Show Abstract
Ensembles of sparse classifiers for osteoporosis characterization in digital radiographs
Paper 10950-76
Author(s): Keni Zheng, Delaware State Univ. (United States); Rachid Jennane, Univ. d'Orléans (France); Sokratis Makrogiannis, Delaware State Univ. (United States)
Show Abstract
Multiclass vertebral fracture classification using probability SVM with multi-feature selection
Paper 10950-77
Author(s): Liyuan Zhang, Huamin Yang, Jiashi Zhao, Weili Shi, Yu Miao, Fei He, Wei He, Yanfang Li, Ke Zhang, Changchun Univ. of Science and Technology (China); Kensaku Mori, Nagoya Univ. (Japan); Zhengang Jiang, Changchun Univ. of Science and Technology (China)
Show Abstract
Session PS2:
Posters: Brain
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Cranial localization in 2D cranial ultrasound images using deep neural networks
Paper 10950-78
Author(s): Pooneh Roshanitabrizi, Children's National Medical Ctr. (United States)
Show Abstract
Learning Imbalanced Semantic Segmentation through Cross-Domain Relations of Multi-Agent Generative Adversarial Networks
Paper 10950-79
Author(s): Mina Rezaei, Hasso-Plattner-Institut (Germany)
Show Abstract
Spatial and depth weighted neural network for diagnosis of Alzheimer’s disease
Paper 10950-80
Author(s): Qingfeng Li, Shanghai United Imaging Intelligence Co., Ltd. (China); Feng Shi, Yiqiang Zhan, Xiang Sean Zhou, Shanghai United Imaging Intelligence, Co., Ltd. (China)
Show Abstract
Effective discrimination of Alzheimer’s disease states using an ensemble neural network’s model
Paper 10950-81
Author(s): Junsik Eom, Hanbyol Jang, Jinseong Jang, Dosik Hwang, Yonsei Univ. (Korea, Republic of)
Show Abstract
Feasibility study of deep neural networks to classify intracranial aneurysms using angiographic parametric imaging
Paper 10950-82
Author(s): Mohammad Mahdi Shiraz Bhurwani, Alexander R. Podgorsak, Anusha Ramesh Chandra, Ryan A. Rava, Univ. at Buffalo (United States), Canon Stroke and Vascular Research Ctr. (United States); Kenneth V. Snyder, Elad I. Levy, Jason M. Davies, 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
Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas
Paper 10950-83
Author(s): Jie Tian, Institute of Automation (China)
Show Abstract
Diagnosis of OCD using functional connectome and Riemann kernel PCA
Paper 10950-84
Author(s): Xiaodan Xing, Shanghai United Imaging Intelligence Co., Ltd. (China); Lili Jin, South China Normal Univ. (China); Feng Shi, Shanghai United Imaging Intelligence Co., Ltd. (China); Ziwen Peng, Shenzhen Kangning Hospital (China)
Show Abstract
Session PS3:
Posters: Breast
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Evaluation of U-net segmentation models for infarct volume measurement in acute ischemic stroke: comparison with fixed ADC threshold-based methods
Paper 10950-85
Author(s): Yoon-Chul Kim, Sungkyunkwan Univ. (Korea, Republic of), SAMSUNG Medical Ctr. (Korea, Republic of); Ji-Eun Lee, Inwu Yu, In-Young Baek, SAMSUNG Medical Ctr. (Korea, Republic of); Han-Gil Jeong, Beom-Joon Kim, Seoul National Univ. Hospital (Korea, Republic of); Joon-Kyung Seong, Korea Univ. (Korea, Republic of); Jong-Won Chung, Oh Young Bang, Woo-Keun Seo, SAMSUNG Medical Ctr. (Korea, Republic of)
Show Abstract
Multi-path deep learning model for automated mammographic density categorization
Paper 10950-86
Author(s): Xiangyuan Ma, Sun Yat-Sen Univ. (China), Univ. of Michigan (United States); Caleb E. Fisher, Jun Wei, Mark A. Helvie, Heang-Ping Chan, Chuan Zhou, Lubomir M. Hadjiiski, Univ. of Michigan (United States); Yao Lu, Sun Yat-Sen Univ. (China)
Show Abstract
Exploratory learning with convolutional autoencoder for discrimination of architectural distortion in digital mammography
Paper 10950-87
Author(s): Helder C. Oliveira, Univ. de São Paulo (Brazil); Carlos F. E. Melo, Clinica Eco & Mama Diagnóstico Digital (Brazil); Juliana H. Catani, Nestor Barros, Marcelo A. C. Vieira, Univ. de São Paulo (Brazil)
Show Abstract
Computationally-efficient wavelet-based characterization of breast tumors using conventional B-mode ultrasound images
Paper 10950-88
Author(s): Manar Mahmoud, Mohamed Salaheldien, Muhammad Rushdi, Cairo Univ. (Egypt); Iman Ewais, Eman Hosny, Hanan Gewefel, Women and Fetal Imaging Ctr. (Egypt); Ahmed Mahmoud, Cairo Univ. (Egypt)
Show Abstract
Breast dispersion imaging using undersampled rapid dynamic contrast-enhanced MRI
Paper 10950-89
Author(s): Linxi Shi, Stanford Univ. (United States)
Show Abstract
Deep learning approach predicting breast tumor response to neoadjuvant treatment using two DCE-MRI exams
Paper 10950-90
Author(s): Mohammed El Adoui, Univ. De Mons (Belgium); Mohamed Amine Larhmam, Univ. de Mons (Belgium); Stylianos Drisis, Institut Jules Bordet (Belgium); Mohammed Benjelloun, Univ. de Mons (Belgium)
Show Abstract
Computer-aided detection and classification of microcalcification clusters on full field digital mammograms using deep convolution neural network
Paper 10950-91
Author(s): Guanxiong Cai, Sun Yat-Sen Univ. (China); Yanhui Guo, Univ. of Illinois at Springfield (United States); Weiguo Chen, Hui Zeng, Southern Medical Univ. (China); Yuanpin Zhou, Yao Lu, Sun Yat-Sen Univ. (China)
Show Abstract
Associations between mammographic phenotypes and histopathologic features in ductal carcinoma in situ
Paper 10950-92
Author(s): Ruvini Navaratna, Aimilia Gastounioti, Meng-Kang Hsieh, Lauren Pantalone, Marie Shelanski, Emily F. Conant, Despina Kontos, Univ. of Pennsylvania (United States)
Show Abstract
The automatic segmentation of mammographic mass using the end-to-end convolutional network based on dense-prediction
Paper 10950-93
Author(s): Lin Zhou, Huazhong Univ. of Science and Technology (China)
Show Abstract
Synthesis and texture manipulation of screening mammograms using conditional generative adversarial network
Paper 10950-94
Author(s): Dehan Kong, Beijing Institute of Technology (China); Rui Hou, Yinhao Ren, Duke Univ. (United States), Duke Univ. School of Medicine (United States); Lars J. Grimm, Jeffrey R. Marks, Duke Univ. School of Medicine (United States); Joseph Y. Lo, Carl E. Ravin Advanced Imaging Labs. (United States)
Show Abstract
Breast MRI radiomics for pre-treatment prediction of pathologic complete response to neoadjuvant chemotherapy in node-positive breast cancer patients
Paper 10950-95
Author(s): Karen Drukker, Iman El-Bawab, Alexandra Edwards, Christopher Doyle, John Papaioannou, Kirti Kulkarni, Maryellen L. Giger, The Univ. of Chicago Medicine (United States)
Show Abstract
Developing a new quantitative imaging marker to predict pathological complete response to neoadjuvant chemotherapy
Paper 10950-96
Author(s): Faranak Aghaei, The Univ. of Oklahoma (United States); Alan B. Hollingsworth, Mercy Health Ctr. (United States); Seyedeh-Nafiseh Mirnia-harikandehei, Yunzhi Wang, Hong Liu, Bin Zheng, The Univ. of Oklahoma (United States)
Show Abstract
Deep learning of sub-regional breast parenchyma in mammograms for localized breast cancer risk prediction
Paper 10950-97
Author(s): Giacomo Nebbia, Aly A. Mohamed, Ruimei Chai, Bingjie Zheng, Univ. of Pittsburgh (United States); Margarita Zuley, Univ. of Pittsburgh Medical Ctr. (United States); Shandong Wu, Univ. of Pittsburgh (United States)
Show Abstract
Malignant microcalcification clusters detection using unsupervised deep autoencoders
Paper 10950-98
Author(s): Rui Hou, Yinhao Ren, Duke Univ. (United States); Lars J. Grimm, Maciej A. Mazurowski, Jeffrey R. Marks, Lorraine 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
Automated deep-learning method for whole-breast segmentation in diffusion-weighted breast MRI
Paper 10950-99
Author(s): Lei Zhang, Aly A. Mohamed, Ruimei Chai, Bingjie Zheng, Shandong Wu, Univ. of Pittsburgh (United States)
Show Abstract
A shell and kernel descriptor based joint deep learning model for predicting breast lesion malignancy
Paper 10950-100
Author(s): Zhiguo Zhou, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); Genggeng Qin, Southern Medical Univ. (China); Pingkun Yan, Rensselaer Polytechnic Institute (United States); Hongxia Hao, Xidian Univ. (China); Steve Jiang, Jing Wang, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Show Abstract
Session PS4:
Posters: Cell
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Automatic cell segmentation using mini-u-net on fluorescence in situ hybridization images
Paper 10950-101
Author(s): Jianhuo Shen, Teng Li, Chuanrui Hu, Anhui Univ. (China); Hong He, Shanghai Yiying Medical Technology (China); Jianfei Liu, Anhui Univ. (China)
Show Abstract
Session PS5:
Posters: Colon
Wednesday 20 February 2019
5:30 PM - 7:00 PM
A pyramid learning model for polyp classification via CT colonography
Paper 10950-102
Author(s): Weiguo Cao, Stony Brook Univ. (United States); Marc J. Pomeroy, Zhengrong Liang, The State Univ. of New York (United States)
Show Abstract
Polyp classification by Weber’s law as texture descriptor for clinical colonoscopy
Paper 10950-103
Author(s): Yi Wang, Tianjin Univ. (China), The State Univ. of New York (United States); Marc J. Pomeroy, Weiguo Cao, Yongfeng Gao, Stony Brook Univ. (United States); Edward Sun, The State Univ. of New York (United States); Samuel Stanley, Washington Univ. in St. Louis (United States); Zhengrong Liang, The State Univ. of New York (United States)
Show Abstract
Texture feature analysis of neighboring colon wall for colorectal polyp classification
Paper 10950-104
Author(s): Marc J. Pomeroy, Almas Abbasi, Kevin Baker, Matthew Barish, Stony Brook Univ. (United States); Samuel Stanley, Washington Univ. in St. Louis (United States); Perry J. Pickhardt, Univ. of Wisconsin-Madison (United States); Zhengrong Liang, Stony Brook Univ. (United States)
Show Abstract
The detection of non-polypoid colorectal lesions using the texture feature extracted from intact colon wall: a pilot study
Paper 10950-105
Author(s): Sang Hainan, Meng Jiang, Yang Liu, Hongbing Lu, Fourth Military Medical Univ. (China)
Show Abstract
Differentiation of polyps by clinical colonoscopy via integrated color information and machine learning
Paper 10950-106
Author(s): Yi Wang, Tianjin Univ. (China), The State Univ. of New York (United States); Marc J. Pomeroy, Weiguo Cao, Yongfeng Gao, Stony Brook Univ. (United States); Edward Sun, The State Univ. of New York (United States); Samuel Stanley, Washington Univ. in St. Louis (United States); Zhengrong Liang, Stony Brook Univ. (United States)
Show Abstract
Session PS6:
Posters: Eyes
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Early detection of retinopathy of prematurity stage using deep learning approach
Paper 10950-107
Author(s): Supriti Mulay, Keerthi Ram, Mohanasankar Sivaprakasam, Indian Institute of Technology Madras (India); Anand Vinekar, Narayana Nethralaya Postgraduate Institute of Ophthalmology (India)
Show Abstract
Longitudinal matching of in vivo adaptive optics images of fluorescent cells in the human eye using stochastically consistent superpixels
Paper 10950-108
Author(s): Jianfei Liu, Hae Won Jung, Tao Liu, Johnny Tam, National Institutes of Health (United States)
Show Abstract
Computer-based detection of age-related macular degeneration and glaucoma using retinal images and clinical data
Paper 10950-109
Author(s): Vinayak Joshi, Jeffrey Wigdahl, Jeremy Benson, Sheila Nemeth, Peter Soliz, VisionQuest Biomedical LLC (United States)
Show Abstract
Fully-automated segmentation of optic disk from retinal images using deep learning techniques
Paper 10950-110
Author(s): Fatemeh Zabihollahy, Eranga Ukwatta, Carleton Univ. (Canada)
Show Abstract
Macular location using improved faster R-CNN
Paper 10950-111
Author(s): Xudong Huang, Weifang Zhu, Yuhui Ma, Heming Zhao, XinJian Chen, Soochow Univ. (China)
Show Abstract
Session PS7:
Posters: Head
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Deep learning-based detection of anthropometric landmarks in 3D infants head models
Paper 10950-112
Author(s): Helena R. Torres, Bruno Oliveira, Fernando Veloso, António Moreira, Pedro Morais, João L. Vilaça, Instituto Politécnico do Cávado e do Ave (Portugal)
Show Abstract
Quantitative evaluation of local head malformations from 3D photography: application to craniosynostosis
Paper 10950-113
Author(s): Liyun Tu, Antonio R. Porras, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System (United States); Albert Oh, Children's National Health System (United States); Natasha Lepore, Children's Hospital Los Angeles (United States), The Univ. of Southern California (United States); Graham C. Buck, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System (United States); Deki Tsering, Children's National Health System (United States); Andinet Enquobahrie, Kitware, Inc. (United States); Robert Keating, Children's National Health System (United States); Gary F. Rogers, Children's National Medical Ctr. (United States); Marius George Linguraru, Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Health System (United States)
Show Abstract
Predicting resection volume in the nasal cavity to estimate surgical outcomes
Paper 10950-114
Author(s): Manuel Berger, Medizinische Univ. Innsbruck (Austria), MCI Management Center Innsbruck Internationale Hochschule GmbH (Austria); Martin Pillei, Friedrich-Alexander-Univ. Erlangen-Nurnberg (Germany), MCI Management Center Innsbruck Internationale Hochschule GmbH (Austria); Andreas Mehrle, MCI Management Center Innsbruck Internationale Hochschule GmbH (Austria); Wolfgang Recheis, Medizinische Univ. Innsbruck (Austria); Florian Kral, Kardinal Schwarzenberg Klinikum GmbH (Austria); Michael Kraxner, MCI Management Center Innsbruck Internationale Hochschule GmbH (Austria); Wolfgang Freysinger, Medizinische Univ. Innsbruck (Austria)
Show Abstract
Session PS8:
Posters: Heart
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Develop MRI post processing protocol for cardiovascular risk detection in asymptomatic diabetic patients
Paper 10950-115
Author(s): Mariam Afshin, Rasha Mahmoud, Alan Moody, Sunnybrook Health Sciences Ctr. (Canada)
Show Abstract
Automated scoring of aortic calcification in vertebral fracture assessment images
Paper 10950-116
Author(s): Luke A. Chaplin, Tim F. Cootes, The Univ. of Manchester (United Kingdom)
Show Abstract
Detection and classification of coronary artery calcifications in low dose thoracic CT using deep learning
Paper 10950-117
Author(s): Jordan Fuhrman, Jennie Crosby, The Univ. of Chicago (United States); Claudia Henschke, David Yankelevitz, Icahn School of Medicine at Mount Sinai (United States); Maryellen L. Giger, The Univ. of Chicago (United States)
Show Abstract
Computerized identification of early ischemic changes in acute stroke in noncontrast CT using deep learning
Paper 10950-118
Author(s): Noriyuki Takahashi, Yuki Shinohara, Toshibumi Kinoshita, Tomomi Ohmura, Keisuke Matsubara, Research Institute for Brain and Blood Vessels - Akita (Japan); Yongbum Lee, Niigata Univ. (Japan); Hideto Toyoshima, Research Institute for Brain and Blood Vessels - Akita (Japan)
Show Abstract
Automated characterization of stenosis in invasive coronary angiography images with convolutional neural networks
Paper 10950-119
Author(s): Benjamin Au, Yale Univ. (United States)
Show Abstract
Fully automated segmentation of left ventricular myocardium from 3D late gadolinium enhancement magnetic resonance images using a U-Net-based convolutional neural network
Paper 10950-120
Author(s): Fatemeh Zabihollahy, Carleton Univ. (Canada); James A. White, Libin Cardiovascular Institute of Alberta (Canada); Eranga Ukwatta, Carleton Univ. (Canada)
Show Abstract
Session PS9:
Posters: Kidneys
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Deep learning based bladder cancer treatment response assessment
Paper 10950-121
Author(s): Eric Wu, Lubomir M. Hadjiiski, Ravi K. Samala, Heang-Ping Chan, Univ. of Michigan (United States); Kenny H. Cha, U.S. Food and Drug Administration (United States); Caleb Richter, Richard H. Cohan, Elaine M. Caoili, Chintana Paramagul, Ajjai Alva, Alon Z. Weizer, Univ. of Michigan (United States)
Show Abstract
Session PS10:
Posters: Liver
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Application of deep learning convolutional neural networks to the estimation of liver fibrosis severity from ultrasound texture
Paper 10950-122
Author(s): Alex Treacher, Daniel Beauchamp, Bilal Quadri, Abhinav Vij, David Fetzer, Takeshi Yokoo, Albert Montillo, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Show Abstract
Session PS11:
Posters: Lung
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Automated identification of thoracic pathology from chest radiographs with enhanced training pipeline
Paper 10950-123
Author(s): Adora M. DSouza, Univ. of Rochester (United States); Anas Z. Abidin, Axel Wismüller, Univ. of Rochester Medical Ctr. (United States)
Show Abstract
3D fully convolutional network-based segmentation of lung nodules in CT images with a clinically inspired data synthesis method
Paper 10950-124
Author(s): Atsushi Yaguchi, Toshiba Corp. (Japan); Kota Aoyagi, Canon Medical Systems Corp. (Japan); Akiyuki Tanizawa, Toshiba Corp. (Japan); Yoshiharu Ohno, Kobe Univ. School of Medicine (Japan)
Show Abstract
A lung graph model for the classification of interstitial lung disease on CT images
Paper 10950-125
Author(s): Guillaume Vanoost, INP-ENSEEIHT (France), Fachhochschule NordWestschweiz (Switzerland); Yashin Dicente Cid, Fachhochschule NordWestschweiz (Switzerland), Univ. de Genève (Switzerland); Adrien Depeursinge, Fachhochschule NordWestschweiz (Switzerland), Ctr. Hospitalier Univ. Vaudois (Switzerland)
Show Abstract
Improving pulmonary lobe segmentation on expiratory CTs by using aligned inspiratory CTs
Paper 10950-126
Author(s): Oliver Weinheimer, Mark O. Wielpütz, Philip Konietzke, Ruprecht-Karls-Univ. Heidelberg (Germany); Claus Peter Heussel, Thoraxklinik Univ. Heidelberg (Germany); Hans-Ulrich Kauczor, Ruprecht-Karls-Univ. Heidelberg (Germany); Terry E. Robinson, Stanford Univ. (United States); Craig J. Galban, Univ. of Michigan (United States)
Show Abstract
Pre-trained deep convolutional neural networks for the segmentation of malignant pleural mesothelioma tumor on CT scans
Paper 10950-127
Author(s): Eyjólfur Gudmundsson, Christopher M. Straus, Samuel G. Armato, The Univ. of Chicago (United States)
Show Abstract
Artificial intelligence for point of care radiograph quality assessment
Paper 10950-128
Author(s): Satyananda Kashyap, IBM Research Almaden (United States); Mehdi Moradi, Alexandros Karargyris, Joy T. Wu, Michael Morris, Babak Saboury, IBM Research - Almaden (United States); Eliot Siegel, Univ. of Maryland (United States); Tanveer Syeda-Mahmood, IBM Research - Almaden (United States)
Show Abstract
Exploring features towards semantic characterization of lung nodules in computed tomography images
Paper 10950-129
Author(s): Maysa M. G. Macedo, Dario A. B. Oliveira, IBM Research - Brazil (Brazil)
Show Abstract
Lung segmentation based on a deep learning approach for dynamic chest radiography
Paper 10950-130
Author(s): Yuki Kitahara, Rie Tanaka, Kanazawa Univ. (Japan); Holger Roth, Hirohisa Oda, Kensaku Mori, Nagoya Univ. (Japan); Kazuo Kasahara, Isao Matsumoto, Kanazawa Univ. Hospital (Japan)
Show Abstract
Computer-aided detection using non-convolutional neural network Gaussian processes
Paper 10950-131
Author(s): Devanshu Agrawal, The Univ. of Tennessee (United States); Jacob D. Hinkle, Hong-Jun Yoon, Georgia Tourassi, Oak Ridge National Lab. (United States)
Show Abstract
2.5D CNN model for detecting lung disease using weak supervision
Paper 10950-132
Author(s): Yue Geng, Duke Univ. School of Medicine (United States), Tsinghua Univ. (China); Yinhao Ren, Rui Hou, Duke Univ. School of Medicine (United States); Songyue Han, South China Univ. of Technology (China); Geoffrey D. Rubin, Joseph Y. Lo, Duke Univ. School of Medicine (United States)
Show Abstract
Lung nodule retrieval using semantic similarity estimates
Paper 10950-133
Author(s): Mark Loyman, Hayit Greenspan, Tel Aviv Univ. (Israel)
Show Abstract
Predicting unnecessary nodule biopsy for a small lung cancer screening dataset by less-abstractive deep features
Paper 10950-134
Author(s): Fangfang Han, Linkai Yan, Chen Li, Shouliang Qi, Northeastern Univ. (China); William Moore, New York Univ. (United States); Zhengrong Liang, The State Univ. of New York (United States); Wei Qian, The Univ. of Texas at El Paso (United States)
Show Abstract
Lung tissue characterization for emphysema differential diagnosis using deep convolutional neural networks
Paper 10950-135
Author(s): Mohammadreza Negahdar, David Beymer, IBM Research - Almaden (United States)
Show Abstract
Fine-grained lung nodule segmentation with pyramid deconvolutional neural network
Paper 10950-136
Author(s): Xinzhuo Zhao, The Univ. of Texas at El Paso (United States), Northeastern Univ. (China); Wenqing Sun, Wei Qian, The Univ. of Texas at El Paso (United States); Shouliang Qi, Northeastern Univ. (China); Jianjun Sun, The Univ. of Texas at El Paso (United States); Bo Zhang, Zhigang Yang, Northeastern Univ. (China)
Show Abstract
Similar CT image retrieval method based on lesion nature and their three-dimensional distribution
Paper 10950-137
Author(s): Yasutaka Moriwaki, Nobuhiro Miyazaki, Hiroaki Takebe, Takayuki Baba, Fujitsu Labs., Ltd. (Japan); Hiroaki Terada, Toru Higaki, Kazuo Awai, Hiroshima Univ. (Japan); Machiko Nakagawa, Akio Ozawa, Fujitsu Ltd. (Japan); Kennji Kitayama, Hiroshima Univ. (Japan); Yasuharu Ogino, Fujitsu Ltd. (Japan)
Show Abstract
Session PS12:
Posters: Lymph Nodes and Thyroid
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Metastatic lymph node analysis of colorectal cancer using quadruple-phase CT images
Paper 10950-138
Author(s): Keisuke Bando, Tokushima Univ. (Japan)
Show Abstract
CT-realistic data augmentation using generative adversarial network for robust lymph node segmentation
Paper 10950-139
Author(s): Youbao Tang, Sooyoun Oh, Yuxing Tang, National Institutes of Health (United States); Jing Xiao, Ping An Insurance (Group) Company of China (China); Ronald M. Summers, National Institutes of Health (United States)
Show Abstract
Transfer learning using inception-resnet-v2 for cancer tissue identification based on PA imaging
Paper 10950-140
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
Session PS13:
Posters: Prostate
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Automatic MRI prostate segmentation using 3D deeply supervised FCN with concatenated atrous convolution
Paper 10950-141
Author(s): Bo Wang, Yang Lei, Jason J. Jeong, Tonghe Wang, Sibo Tian, Pretesh Patel, Xiaojun Jiang, Ashesh B. Jani, Hui Mao, Walter J. Curran, Tian Liu, Xiaofeng Yang, Emory Univ. (United States)
Show Abstract
Radiomic features derived from multi-parametric MRI of the prostate may predict risk of metastasis obtained from decipher score
Paper 10950-142
Author(s): Lin Li, Rakesh Shiradkar, Ahmad Algohary, Case Western Reserve Univ. (United States); Cristina Magi-Galluzzi, Eric Klein, Andrei Purysko, Cleveland Clinic (United States); Anant Madabhushi, Case Western Reserve Univ. (United States)
Show Abstract
Session PS14:
Posters: Radiomics
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Towards deep radiomics: nodule malignancy prediction using CNNs on feature images
Paper 10950-143
Author(s): Rahul Paul, Dmitry Cherezov, Univ. of South Florida (United States); Matthew Schabath, Robert Gillies, H. Lee Moffitt Cancer Ctr. & Research Institute (United States); Lawrence Hall, Dmitry Goldgof, Univ. of South Florida (United States)
Show Abstract
Radiomics analysis on T2-MR image to predict lymphovascular space invasion in cervical cancer
Paper 10950-144
Author(s): Shou Wang, Institute of Automation (China); Xi Chen, Beijing Institute of Technology (China); Zhenyu Liu, Institute of Automation (China); Qingxia Wu, Henan Provincial People’s Hospital (China); Yongbei Zhu, Institute of Automation (China); Meiyun Wang, Henan Provincial People's Hospital (China); Jie Tian, Institute of Automation (China)
Show Abstract
Temporal mammographic registration for evaluation of architecture changes in cancer risk assessment
Paper 10950-145
Author(s): Kayla R. Mendel, Hui Li, The Univ. of Chicago (United States); Nabihah Tayob, The Univ. of Texas M. D. Anderson Cancer Ctr. (United States); Randa El-Sein, Houston Methodist (United States); Isabelle Bedrosian, The Univ. of Texas M. D. Anderson Cancer Ctr. (United States); Maryellen L. Giger, The Univ. of Chicago (United States)
Show Abstract
PI-RADS guided discovery radiomics for characterization of prostate lesions with diffusion-weighted MRI
Paper 10950-146
Author(s): Farzad Khalvati, Univ. of Toronto (Canada), Sinai Health System (Canada); Yucheng Zhang, Phuong H. U. Le, Univ. of Toronto (Canada); Isha Gujrathi, Sinai Health System (Canada); Masoom A. Haider, Univ. of Toronto (Canada)
Show Abstract
Non-invasive transcriptomic classification of de novo Glioblastoma patients through multivariate quantitative analysis of baseline preoperative multimodal magnetic resonance imaging
Paper 10950-147
Author(s): Saima Rathore, Hamed Akbari, Spyridon Bakas, Perelman School of Medicine, Univ. of Pennsylvania (United States); Jared Pisapia, Children's Hospital of Philadelphia (United States); Xiao Da, Brigham and Women's Hospital, Harvard Univ. (United States); Donald M. O’Rourke, Christos Davatzikos, Perelman School of Medicine, Univ. of Pennsylvania (United States)
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Radiomics analysis of MRI for predicting molecular subtypes of young breast cancer
Paper 10950-148
Author(s): Qinmei Li, The Univ. of Texas at Dallas (United States), The Second Affiliated Hospital of Guangzhou Medical Univ. (China); James D. Dormer, Priyanka Daryani, The Univ. of Texas at Dallas (United States); Deji Chen, Zhenfeng Zhang, The Second Affiliated Hospital of Guangzhou Medical Univ. (China); Baowei Fei, The Univ. of Texas at Dallas (United States), The Univ. of Texas Southwestern Medical Ctr. (United States)
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Tumor connectomics: mapping the intra-tumoral complex interaction network
Paper 10950-149
Author(s): Vishwa S. Parekh, Michael A. Jacobs, Johns Hopkins Univ. (United States)
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General purpose radiomics for multi-modal clinical research
Paper 10950-150
Author(s): Michael G. Wels, Félix Lades, Alexander Muehlberg, Michael Suehling, Siemens Healthineers (Germany)
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Quantitative MRI biomarker for treatment response assessment of multiple myeloma: robustness evaluation using independent test set of prospective cases
Paper 10950-151
Author(s): Chuan Zhou, Qian Dong, Heang-Ping Chan, Erica L. Campagnaro, Jun Wei, Lubomir M. Hadjiiski, Univ. of Michigan Health System (United States)
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Machine-learning-based classification of low-grade and high-grade Glioblastoma using radiomic features in multiparametric MRI
Paper 10950-152
Author(s): Ge Cui, Jason J. Jeong, Yang Lei, Tian Liu, Walter J. Curran, Hui Mao, Xiaofeng Yang, Emory Univ. (United States)
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Prediction of low-grade glioma progression using MR imaging
Paper 10950-160
Author(s): Zeina Shboul, Khan Iftekharuddin, Old Dominion Univ. (United States)
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Radiomics and deep learning of diffusion-weighted MRI in the diagnosis of breast cancer
Paper 10950-161
Author(s): Qiyuan Hu, The Univ. of Chicago (United States); Heather M. Whitney, The Univ. of Chicago (United States), Wheaton College (United States); Alexandra Edwards, John Papaioannou, Maryellen L. Giger, The Univ. of Chicago (United States)
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Session PS15:
Posters: Skin
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Acral melanocytic lesion segmentation with a convolution neural network (U-Net)
Paper 10950-153
Author(s): Joanna Jaworek-Korjakowska, AGH Univ. of Science and Technology (Poland)
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Session PS16:
Posters: Staging
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Dose distribution as outcome predictor for Gamma Knife radiosurgery on vestibular schwannoma
Paper 10950-154
Author(s): Patrick Langenhuizen, ETZ Elisabeth (Netherlands), Technische Univ. Eindhoven (Netherlands); Hans van Gorp, Svetlana Zinger, Technische Univ. Eindhoven (Netherlands); Jeroen Verheul, ETZ Elisabeth (Netherlands); Sieger Leenstra, Erasmus MC (Netherlands); Peter H. N. de With, Technische Univ. Eindhoven (Netherlands)
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Session PS17:
Posters: Vascular
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Learning-based automatic segmentation on arteriovenous malformations from contract-enhanced CT images
Paper 10950-155
Author(s): Tonghe Wang, Yang Lei, Ghazal Shafai-Erfani, Xiaojun Jiang, Xue Dong, Anees Dhabaan, Tian Liu, Walter J. Curran, Hui-Kuo Shu, Xiaofeng Yang, Emory Univ. (United States)
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Use of a convolutional neural network for aneurysm identification in digital subtraction angiography
Paper 10950-156
Author(s): Alexander R. Podgorsak, Mohammad Mahdi Shiraz Bhurwani, Ryan A. Rava, Anusha Ramesh Chandra, Ciprian N. Ionita, Canon Stroke and Vascular Research Ctr. (United States)
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U-Net based automatic carotid plaque segmentation from 3D ultrasound images
Paper 10950-157
Author(s): Ran Zhou, Wei Ma, Huazhong Univ. of Science and Technology (China); Aaron Fenster, Western Univ. (Canada); Mingyue Ding, Huazhong Univ. of Science and Technology (China)
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Session PS18:
Posters: New Approaches
Wednesday 20 February 2019
5:30 PM - 7:00 PM
Machine learning for segmenting cells in corneal endothelium images
Paper 10950-158
Author(s): Chaitanya Kolluru, Beth Benetz, Naomi Joseph, Hao Wu, Harry Menegay, Case Western Reserve Univ. (United States); Jonathan Lass, Univ. Hospitals (United States); David Wilson, Case Western Reserve Univ. (United States)
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Classifying abnormalities in computed tomography radiology reports with rule-based and natural language processing models
Paper 10950-159
Author(s): Songyue Han, James Tian, Mark Kelly, Vignesh Selvakumaran, Ricardo Henao, Geoffrey D. Rubin, Joseph Y. Lo, Duke Univ. (United States)
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