AI in Healthcare

Research and innovation addressing today's greatest health challenges.

We believe that medicine can greatly benefit from technologies that learn from the rich clinical and wellness data being collected nowadays in vast quantities. Our vision is that combining medical knowledge with technical eminence in machine learning, deep learning, causal inference, and decision analytics applied to the right data can result in better sustainable care. To achieve this ambitious goal, we partner with leaders from the local and global ecosystem such as Maccabi Health, Sagol Neuroscience School at Tel Aviv University, Teva Pharmaceuticals, University of Miami Health System, and many others.

Our research technologies include:

  • Machine learning
  • Causal analysis
  • Medical imaging analysis
  • Bioinformatics
  • Cognitive computing
  • Deep learning
  • Image analytics

What's special about what we do?

Our team works on researching seed ideas for innovative healthcare technologies and bringing them to fruition for IBM clients. Our work is about innovation and exploiting data to gain smarter insights. We support pharmaceutical companies in designing and studying clinical trials, and specifically understanding post-launch patient data (Real World Evidence studies). This helps health policy makers carry out more informed decisions on how to prevent and proactively address particular health conditions. It can also help physicians make better decisions and diagnoses. We can analyze diverse data including radiology images, text data, structured data, and sensor data.

Latest COVID-19 research


Michal Rosen-Zvi, Director of Healthcare Informatics for IBM Research, and Department General Manager of AI in Healthcare at IBM Research - Haifa

Michal Rosen-Zvi,
Director, Healthcare Informatics for IBM Research,
Department General Manager of AI in Healthcare,
IBM Research - Haifa


Main Focus

Machine Learning for Healthcare and Life Sciences


Medical Imaging Analytics and Solutions



Title Author Conference/Journal Year Focus  
Framework for Identifying Drug Repurposing Candidates from Observational Healthcare Data Ozery-Flato, Michal, et al. JAMIA Open (2020) 2020 Machine Learning 
How the weather affects the pain of citizen scientists using a smartphone app Dixon, William G., et al. NPJ digital medicine 2.1 (2019): 1-9. 2019 Machine LearningSelected Work
Comment: Causal Inference Competitions: Where Should We Aim? E.Karavani , T.El-Hay , Y. Shimoni , C.Yanover Statistical Science 34.1 (2019): 86-89. 2019 Machine Learning 
Inferring new relations between medical entities using literature curated term co-occurrences Spiro, Adam, Jonatan Fernández García, and Chen Yanover. JAMIA open 2.3 (2019): 378-385. 2019 Machine Learning 
Framework for reliable value assessment of treatments using causal analysis of observational data: support matching biological therapy to rheumatoid arthritis patients Y. Shimoni, S. Ravid, P. Bak, E. Karavani, S. Hensley Alford, D. Meade, Y. Goldschmidt Value in Health 22, S389 (2019). 2019 Machine LearningSelected Work
A discriminative approach for finding and characterizing positivity violations using decision trees Karavani, Ehud, Peter Bak, and Yishai Shimoni. arXiv preprint arXiv:1907.08127 (2019). 2019 Machine Learning 
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference Shimoni, Yishai, et al. arXiv preprint arXiv:1906.00442 (2019). 2019 Machine Learning 
Factorial HMMs with Collapsed Gibbs Sampling for Optimizing Long-term HIV Therapy Amit Gruber, Chen Yanover, Tal El-Hay, Anders Sonerborg, Francesca Incardona, Yaara Goldschmidt International Conference on Artificial Intelligence and Statistics. 2018. 2018 Machine Learning 
Adversarial balancing for causal inference Ozery-Flato, Michal, et al. arXiv preprint arXiv:1810.07406 (2018). 2018 Machine Learning 
Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification Shimoni, Yishai. PLoS computational biology 14.2 (2018): e1006026. 2018 Machine Learning 
Characterizing Subpopulations with Better Response to Treatment Using Observational Data-an Epilepsy Case Study Ozery-Flato, Michal, et al. bioRxiv (2018): 290585. 2018 Machine LearningSelected Work
Association between expression of random gene sets and survival is evident in multiple cancer types and may be explained by sub-classification Yishai Shimoni PLoS Comput Biol. 2018 Feb 22;14(2):e1006026. 2018 Machine Learning 
Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis Yishai Shimoni, Chen Yanover , Ehud Karavani , and Yaara Goldschmnidt arXiv preprint arXiv:1802.05046 (2018). 2018 Machine Learning 
Estimating Effects Of Second Line Therapy For Type 2 Diabetes Mellitus: Retrospective Cohort Study Assaf Gottlieb, Chen Yanover, Amos Cahan, Yaara Goldschmidt, BMJ Open Diabetes Research and Care, 5:e000435, 2017 BMJ Open Diabetes Research and Care, 5:e000435, Year:2017 2017 Machine LearningSelected Work
Using a Data-Driven Policy Decision Support Tool Michal Chorev, Lavi Shpigelman, Peter Bak, Avi Yaeli, Edwin Michael, Ya’ara Goldschmidt MedInfo 2017 2017 Machine Learning 
A Data-Driven Decision-Support Tool for Population Health Policies Chorev M, Shpigelman L, Bak P, Yaeli A, Michael E, Goldschmidt Y Stud Health Technol Inform.2017;245:332-336. 2017 2017 Machine Learning, Medical ImagingSelected Work
Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data Michal Ozery-Flato, Chen Yanover, Assaf Gottlieb, Omer Weissbrod, Naama Parush Shear-Yashuv, and Yaara Goldschmidt Stud Health Technol Inform.235:181-185, 2017 2017 Machine LearningSelected Work
Epidemiological models without process noise are probably over confident Lavi Shpigelman, Michal Chorev, Zeev Waks, Ya’ara Goldschmidt, Edwin Michael. Stud Health Technol Inform.235:136-140. 2017 2017 Machine Learning 
Changes in Vaginal Community State Types Reflect Major Shifts in the Microbiome J. Paul Brooks, Gregory A. Buck, Guanhua Chen, Liang Diao, David J. Edwards, Jennifer M. Fettweis, Snehalata Huzurbazar, Alexander Rakitin, Glen A. Satten, Ekaterina Smirnova, Zeev Waks, Michelle L. Wright, Chen Yanover, Yi-Hui Zhou Microbial Ecology in Health and Disease, 2017 2017 Machine Learning 
Integrated multisystem analysis in a mental health and criminal justice ecosystem Falconer E, El-Hay T, Alevras D, Docherty J, Yanover C, Kalton A, Goldschmidt, Rosen-Zvi Health & Justice 5:4 2017 2017 Machine Learning 
Paradoxical Hypersusceptibility of Drug-resistant Mycobacteriumtuberculosis to β-lactam Antibiotics Cohen KA, El-Hay T, Wyres KL, Weissbrod O, Munsamy V, Yanover C, Aharonov R, Shaham O, Conway TC, Goldschmidt Y, Bishai WR, Pym AS. EBioMedicine 2016 2016 Machine Learning 
Changing the approach to treatment choice in epilepsy using big data Devinsky O, Dilley C, Ozery-Flato M, Aharonov R, Goldschmidt Y, Rosen-Zvi M, Clark C, Fritz P Epilepsy and Behavior, 2016 2016 Machine LearningSelected Work
Global epidemiology of drug resistance following failure of WHO recommended first line regimens for adult HIV-1 infection - an international collaborative study Gregson J. et al The TenoRes Study Group Lancet Infectious Diseases, 2016 2016 Machine Learning 
Identifying and Investigating Unexpected Response to Treatment: A Diabetes Case Study Michal Ozery-Flato, Liat Ein-Dor, Naama Parush-Shear-Yashuv, Ranit Aharonov, Hani Neuvirth, Martin S. Kohn, and Jianying Hu.. BigData2016 2016 Machine Learning 
Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins Zeev Waks, Omer Weissbrod, Boaz Carmeli, Raquel Norel, Filippo Utro, Yaara Goldschmidt. Scientific Reports 6, 2016, Article number: 38988, 2016 2016 Machine Learning 
A Novel Computational Tool for Mining Real-Life Data: Application in the Metastatic Colorectal Cancer Care Setting Siegelmann-Danieli, Farkash, Katzir, Vesterman Landes, Rotem Rabinovich, Lomnicky, Carmeli, Parush-Shear-Yashuv. Plos One 2016 2016 Machine Learning 
Healthcare innovations and improvements in a financially constrained environment The WE CARE consortium. Inger Ekman, Reinhard Busse, Ewout van Ginneken, Chris Van Hoof, Linde van Ittersum,Ab Klink, Jan A. Kremer, Marisa Miraldo, Anders Olauson, Walter De Raedt, Michal Rosen-Zvi, Valentina Strammiello, Jan Törnell, Karl Swedberg, Volume 387, No. 10019, p646–647, 2016 2016 Machine Learning 
A system for identifying and investigating unexpected response to treatment Michal Ozery-Flato, Liat Ein-Dor, Hani Neuvirth, Naama Parush, Martin S. Kohn, Jianying Hu, and Ranit Aharonov, AMIA Jt Summits Transl Sci Proc. 137–141. 2015 2015 Machine Learning 
Integrated multisystem analysis in a mental health and criminal justice ecosystem Falconer E, El-Hay T, Alevras D, Docherty J, Yanover C, Kalton A, Goldschmidt, Rosen-Zvi AMIA Annu Symp Proc. 2014 Nov 14;2014:526-33. eCollection 2014. 2014 Machine Learning 
Structured Proportional Jump Processes Tal El-Hay, Omer Weissbrod, Elad Eban, Maurizio, Francesca Incardona UAI 2014 2014 Machine LearningSelected Work
Paradoxical Hypersusceptibility to Beta-Lactams in Drug Resistant Clinical isolates of M. tuberculosis: Elucidation of Molecular Mechanisms by Whole Genome Sequencing Keira A. Cohen, Tal El-Hay, Kelly L. Wyres, Omer Weissbrod, Tom Conway, Vanisha Munsamy, Max R. O’Donnell, Nesri Padayatchi, Dale Nordenberg, Gail H. Cassell, William R. Bishai and Alexander S. Pym merican Thoracic Society (ATS) international conf. May 2014 2014 Machine Learning 
Estimating the Impact of Prevention Action: A simulation Model of Cervical Cancer Progression Michal Rosen-Zvi, Lavi Shpigelman, Alan Kalton, Omer Weissbrod, Saheed Akindeinde, Soren Benefeldt, Andrew Bentley, Terry Everett, Joseph Jajinskiji, Emmanuel Kweyu, Chalapathy Neti, Joe Saab, Osamuyimen Stewart, Malcolm Ward, Guo Tong Xie, Studies in health technology and informatics 2014; 205, 288-292 2014 Machine Learning 
Redundancy-weighting for better inference of protein structural features Chen Yanover, Natalia Vanetik, Michael Levitt, Rachel Kolodny, and Chen Keasar Bioinformatics 15;30(16):2295-301, 2014 2014 Machine Learning 
Predictive models for type 2 diabetes onset in middle-aged subjects with the metabolic syndrome Ozery-Flato M, Parush N, El-Hay T, Visockienė Z, Ryliškytė L, Badarienė J, Solovjova S, Kovaitė M, Navickas R, Laucevičius A.. Diabetol Metab Syndr. 15;5(1):36. 2013 2013 Machine Learning 
Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents McCormack SE, Shaham O, McCarthy MA, Deik AA, Wang TJ, Gerszten RE, Clish CB, Mootha VK, Grinspoon SK, Fleischman A. Pediatr Obes. 2013 Feb;8(1):52-6, 2013 2013 Machine Learning 
Predicting Response to Antiretroviral Treatment by Machine Learning: The EuResist Project M. Zazzi, F. Incardona, M. Rosen-Zvi, M. Prosperi, T. Lengauer, A. Altmann, A. Sonnerborg, T. Lavee, E. Schülter, and R. Kaiser Intervirology, 55 pp.123-127, 2012 2012 Machine Learning 
Medical image management using service-oriented architecture. Shaham O, Melament A, Barak-Corren Y, Kostirev I, Shmueli N, Peres Y. Flexible Stud Health Technol Inform, 180:1000-4. 2012 2012 Machine Learning 
Estimating BOLD Signals of Deep Brain Networks from EEG using Canonical Correlation Analysis Tal El Hay, Sivan Kinreich, Noam Slonim, Ilana Podlipsky, Talma Hendler, Lavi Shpigelman Organization for Human Brain Mapping, 2012 2012 Machine Learning 
Information-Based Sequential Selection of Clinical Tests in Risk Assessment N. Parush, T. El-Hay, M. Ozery-Flato, L. Ryliskyte, Z. Visockiene, and A. Laucevicius Stud Health Technol Inform.180:781-5, 2012. 2012 Machine Learning 
Toward personalized care management of patients at risk - The diabetes case study H. Neuvirth, M. Ozery-Flato, J. Laserson, M. Rosen-Zvi, J. Hu, M. S. Kohn, S. Ebadollahi The 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011 2011 Machine LearningSelected Work
Declining differences in treatment outcome among different patient categories in Europe, 1992-2010 M. Rosen-Zvi, M. Zazzi, F. Incardona, E. Aharoni, R. Kaiser, T. Lengauer, B. Clotet, A. M. Vandamme, J. C, Schmit A, Sonnerborg 9th European Workshop on HIV & Hepatitis - Treatment Strategies & Antiviral Drug Resistance March 2011 2011 Machine Learning 
Large-scale analysis of chromosomal aberrations in cancer karyotypes reveals two distinct paths to aneuploidy R. Shamir, M. Ozery-Flato, C. Linhart, L. Trachtenbrot and S. Izraeli Genome Biology, 12(6):R61. 2011 2011 Machine Learning 
Antiretroviral Therapy Optimisation without Genotype Resistance Testing: A Perspective on Treatment History Based Models Mattia C. F. Prosperi, Michal Rosen-Zvi, André Altmann, Maurizio Zazzi, Simona Di Giambenedetto, Rolf Kaiser, Eugen Schülter, Daniel Struck, Peter Sloot, David A. van de Vijver, Anne-Mieke Vandamme, Anders Sönnerborg, for the EuResist and Virolab study groups Plos One 2010 2010 Machine Learning 
Prediction of Response to Antiretroviral Therapy by Human Experts and by the EuResist Data-Driven Expert System (the EVE Study) M. Zazzi, R. Kaiser, A. Sonnerborg, D. Struck, A. Altmann, M. Prosperi, M. Rosen-Zvi, A. Petroczi, Y. Peres, E. Schulter, C. Boucher, F. Brun-Vezinet, R. Harigan, L. Morris, M. Obermeier, C. F. Perno, R. Shafer, A. Vandamme, K. van Laethem, A. Wensing, T. Lengauer, F. Incardona HIV Medicine, 2010 2010 Machine Learning 
Investigation of Expert Rule Bases, Logistic Regression, and Non-Linear Machine Learning Techniques for Predicting Response to Antiretroviral Treatment M. CF Prosperi, A. Altmann, M. Rosen-Zvi, E. Aharoni, G. Borgulya, F. Bazso, A. Sonnerborg, Y. Peres, E. Schuflter, D. Struck, G. Ulivi, F. Incardona, A.-M. Vandamme, J. Vercauteren and M. Zazzi for the EuResist and Virolab study groups Antiviral Therapy, 14: 433-442, 2009 2009 Machine Learning 
HIV-1 Drug Resistance Prediction and Therapy Optimization: A Case Study for the Application of Classification and Clustering Methods M. Rosen-Zvi, E. Aharoni, and J. Selbig, M. Biehl et al.: (Eds.): Similarity-Based Clustering, LNAI 5400, pp. 185-201, Springer-Verlag Berlin Heidelberg. 2009 2009 Machine Learning 
Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy A. Altmann, M. Rosen-Zvi, M. Prosperi, E. Aharoni, H. Neuvirth, E. Schulter, J. Buch, D. Struck, Y. Peres, F. Incardona, A. Sonnerborg, R. Kaiser, M. Zazzi, T. Lengauer PLoS ONE 3(10), 2008 2008 Machine Learning 
Selecting anti-HIV therapies based on a variety of genomic and clinical factors M. Rosen-Zvi, A. Altmann, M. Prosperi, E. Aharoni, H. Neuvirth, A. Sonnerborg, E. Shulter, D. Struck, Y. Peres, F. Incardona, R. Kaiser, M. Zazzi, T. Lengauer ISMB conference/ bioinformatics journal 2008. 2008 Machine Learning 
Improving the Performance and Explainability of Mammogram Classifiers with Local Annotations L. Ness, E.Barkan, M.Ozery-Flato accepted to iMIMIC Workshop, MICCAI 2020 2020 Medical Imaging 
Multi-task learning for detection and classification of cancer in screening mammography Maria V. Sainz de Cea, Karl Diedrich, Ran Bakalo, Lior Ness, David Richmond MICCAI 2020 2020 Medical ImagingSelected Work
Radiomics for predicting response to neoadjuvant chemotherapy treatment in breast cancer Simona Rabinovici-Cohen, Tal Tlusty, Ami Abutbul, Kari Antila, Xosé Fernandez, Beatriz Grandal Rejo, Efrat Hexter, Oliver Hijano Cubelos, Abed Khateeb, Juha Pajula, Shaked Perek Proceedings of SPIE 11318 Medical Imaging, Houston, Texas, United States, 2020 2020 Medical Imaging 
Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms Thomas Schaffter, Diana SM Buist, Christoph I Lee, Yaroslav Nikulin, Dezso Ribli, Yuanfang Guan, William Lotter, Zequn Jie, Hao Du, Sijia Wang, Jiashi Feng, Mengling Feng, Hyo-Eun Kim, Francisco Albiol, Alberto Albiol, Stephen Morrell, Zbigniew Wojna, Mehmet Eren Ahsen, Umar Asif, Antonio Jimeno Yepes, Shivanthan Yohanandan, Simona Rabinovici-Cohen, Darvin Yi, Bruce Hoff, Thomas Yu, Elias Chaibub Neto, Daniel L Rubin, Peter Lindholm, Laurie R Margolies, Russell Bailey McBride, Joseph H Rothstein, Weiva Sieh, Rami Ben-Ari, Stefan Harrer, Andrew Trister, Stephen Friend, Thea Norman, Berkman Sahiner, Fredrik Strand, Justin Guinney, Gustavo Stolovitzky, Lester Mackey, Joyce Cahoon, Li Shen, Jae Ho Sohn, Hari Trivedi, Yiqiu Shen, Ljubomir Buturovic, Jose Costa Pereira, Jaime S Cardoso, Eduardo Castro, Karl Trygve Kalleberg, Obioma Pelka, Imane Nedjar, Krzysztof J Geras, Felix Nensa, Ethan Goan, Sven Koitka, Luis Caballero, David D Cox, Pavitra Krishnaswamy, Gaurav Pandey, Christoph M Friedrich, Dimitri Perrin, Clinton Fookes, Bibo Shi, Gerard Cardoso Negrie, Michael Kawczynski, Kyunghyun Cho, Can Son Khoo, Joseph Y Lo, A Gregory Sorensen, Hwejin Jung Journal of the American Medical Association (JAMA) Network Open, 2020 2020 Medical ImagingSelected Work
Multimodal Prediction of Breast Cancer Relapse Prior to Neoadjuvant Chemotherapy Treatment Simona Rabinovici-Cohen, Ami Abutbul, Xosé Fernandez, Oliver Hijano Cubelos, Shaked Perek, Tal Tlusty PRIME-MICCAI Workshop, 2020 2020 Medical Imaging 
The case of missed cancers: Applying AI as a radiologist’s safety net Michal Chorev,Yoel Shoshan, Adam Spiro, Shaked Naor, Alon Hazan, Vesna Barros, Iuliana Weinstein, Esma Herzel, Varda Shalev, Michal Guindy,Michal Rosen-Zvi MICCAI 2020 2020 Medical Imaging, Machine LearningSelected Work
Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammography Images Ayelet Akselrod-Ballin, Michal Chorev , Yoel Shoshan, Adam Spiro, Alon Hazan, Roie Melamed, Ella Barkan, Esma Herzel, Shaked Naor, Ehud Karavani, Gideon Koren, Yaara Goldschmidt, Varda Shalev, Michal Rosen-Zvi, Michal Guind Radiology 292.2 (2019): 331-342, was presented also at "Best of RADIOLOGY in 2019 - The Editors of Radiology keep you up to date" at RSNA 2019 2019 Medical Imaging, Machine LearningSelected Work
Using Deep Learning to improve Efficiency of Breast Cancer Tomosynthesis Screening F. Gilboa-Solomon, R. Bakalo, E. Barkan, Y. Shoshan RSNA 2019 2019 Medical Imaging 
Mammogram Classification with Ordered Loss R. Ben-Ari, Y. Shoshan, T. Tlusty AIME 2019 2019 Medical Imaging 
Learning from Longitudinal Mammography Studies S. Perek, L. Ness, M. Amit, E. Barkan, G. Amit MICCAI 2019 2019 Medical ImagingSelected Work
Automatically detecting data drift in machine learning classifiers O.Raz, M. Zalmanovici , A. Zlotnick , E. Farchi, O. Raz EDSMLS'19 2019 Medical Imaging 
Classification and Detection in Mammograms with Weak Supervision via Dual Branch Deep Neural Net R. Bakalo, R. Ben-Ari, J. Goldberger ISBI 2019 2019 Medical Imaging 
Mammography Dual View Mass Correspondence S. Perek, A. Hazan, E. Barkan, A. Akselrod Ballin KDD Workshop 2018 2018 Medical Imaging 
Unsupervised Clustering of Mammograms for Outlier Detection and Breast Density Estimation R. Ben-Ari, T. Tlusty and G.Amit ICPR 2018 2018 Medical Imaging 
Siamese Network for Dual-View Mammography Mass Matching S. Perek, A. Hazan, E. Barkan, A. Akselrod Ballin MICCAI BIA workshop 2018 2018 Medical Imaging 
Digital Mammography DREAM Challenge: The Core of Top Performing Methods, Special Session R. Ben-Ari Biomedical Health Informatics, 2018 2018 Medical Imaging 
Weakly Supervised Classification and Localization in Mammograms via Dual Branch Deep Network R. Bakalo, J. Goldberger and R. Ben-Ari IMVC 2018 2018 Medical Imaging 
Regularized Adversarial Examples for Model Interpretability Yoel Shoshan, Vadim Ratner arXiv, November 2018 2018 Medical Imaging 
Learning Multiple Non-Mutually-Exclusive Tasks for Improved Classification of Inherently Ordered Labels Vadim Ratner, Yoel Shoshan, Tal Kachman arXiv May 2018 2018 Medical Imaging 
AdapterNet - Learning Input Transformation for Domain Adaptation Alon Hazan, Yoel Shoshan, Daniel Khapun, Roy Aladjem, Vadim Ratner arXiv, May 2018 2018 Medical Imaging 
Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography Akselrod-Ballin, Ayelet; Karlinsky, Leonid.; Hazan, Alon; Bakalo, Ran; Horesh, Ami Ben; Shoshan, Yoel; Barkan, Ella Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support 2017 Medical Imaging 
Weakly  Supervised DNN with AUC Loss for Classifcation of Imbalanced Mammogram Datasets J. Sulam, R. Ben-Ari and P. Kisilev EG VCBM 2017 2017 Medical Imaging 
Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography A. Akselrod-Ballin, L. Karlinsky, A. Hazan, R. Bakalo, E. Barkan, A. Ben-Horesh DLMIA MICCAI 2017 2017 Medical Imaging 
Mammogram Classification and Abnormality Detection from Nonlocal Labels using Deep Multiple Instance Neural Network Y. Choukroun, R. Bakalo, R. Ben-Ari, A. Akselrod-Ballin, E. Barkan and P. Kisilev EG VCBM 2017 2017 Medical Imaging 
A CNN based Method for Mass Detection and Classification in Breast Mammography A. Akselrod-Ballin, L. Karlinsky, S. Alpert, S. Hasoul, E. Barkan Comp. in Bio. and Bio. Eng. Imag.  & Vis. 2017 2017 Medical Imaging 
Automatic Reporting of Lesion Location in Mammograms G. Amit, E. Barkan, N. M. Shani, A. Zlotnick, M. D. Kovacs, J. J. Reicher, M. A. Trambert, M. A. Reicher Computer Assisted Radiology and Surgery (CARS), 2017 2017 Medical Imaging 
Hybrid Mass Detection in Breast MRI combining Unsupervised Saliency Analysis and Deep Learning G. Amit et al MICCAI 2017 2017 Medical Imaging 
Classification of Breast MRI Lesions using Small-Size Training Sets: Comparison of Deep Learning Approaches Amit, G., Ben-Ari, R., Hadad, O., Monovich, E., Granot, N. and Hashoul, S In SPIE Medical Imaging (pp. 101341H-101341H). International Society for Optics and Photonics. 2017, March 2017 Medical Imaging 
Classification of Breast Lesions using Cross-Modal Deep Learning O. Hadad, R. Bakalo, R. Ben-Ari, S. Hashoul, G. Amit ISBI 2017 2017 Medical Imaging 
Domain Specific Convolutional Neural Nets for Detection of Architectural Distortion in Mammograms R. Ben-Ari, A. Akselrod-Ballin, L. Karlinsky, S. Hashoul ISBI 2017 2017 Medical Imaging 
Medical sieve: a cognitive assistant for radiologists and cardiologists Syeda-Mahmood, T.; Walach, E.; Beymer, D.; Gilboa-Solomon, F.; Moradi, M.; Kisilev, P.; Kakrania, D.; Compas, C.; Wang, H.; Negahdar, R.; Cao, Y.; Baldwin, T.; Guo, Y.; Gur, Y.; Rajan, D.; Zlotnick, A.; Rabinovici-Cohen, S.; Ben-Ari, R.; Guy, Amit; Prasanna, P.; Morey, J.; Boyko, O.; Hashoul, S. Medical Imaging 2016: Computer-Aided Diagnosis 2016 Medical Imaging 
Medical image description using multi-task-loss CNN P. Kisilev, E. Sason, S. Hashoul, E. Barkan, E. Walach MICCAI DLMIA 2016 Medical Imaging 
Recognizing Architectural Distortion in Mammogram using pre-trained DNN R. Ben-Ari and S. Hashoul IBM Deep Learning Workshop, 2016 2016 Medical Imaging 
Efficacy of an Automatic Decision Support System in Facilitating Diagnosis of Breast Diseases S. Hashoul, E. Walach, E. Barkan, P. Kisilev, S. Alpert, G. Amit and A. Khateeb European Congress of Radiology,2016 2016 Medical Imaging 
A Weakly Labeled Approach for Breast Tissue Segmentation and Breast Density Estimation in Digital Mammography R. Ben-Ari, A. Zlotnick and S. Hashoul ISBI 2016 2016 Medical Imaging 
NuC-MKL: A Convex Approach to Non Linear Multiple Kernel Learning P. Kisilev and E. Meirom AISTATS 2016 2016 Medical Imaging 
Semantic Object Boundary Detection Using Convolutional Neural Networks with Regression Output P. Kisilev and E. Sason IBM Deep Learning Workshop, 2016 2016 Medical Imaging 
A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography A. Akselrod-Balin, L. Karlinsky, S. Alpert, S. Hashoul, R. Ben-Ari and E. Barkan MICCAI DLMIA workshop, 2016 2016 Medical Imaging 
Learning to describe medical images using multi-task-loss CNN P. Kisilev, E. Sason, S. Hashoul and E. Barkan ECML, 2016 2016 Medical Imaging 
Efficacy of an Automatic Decision Support System in Facilitating Diagnosis of the Thyroid Diseases S. Hashoul, E. Walach, A. Khateeb, A. Walach, G. Amit, R. Ben-Ari, E. Barkan and P. Kisilev RSNA, 2016 2016 Medical Imaging 
Medical image description using multi-task-loss CNN P. Kisilev, E. Sason, S. Hashoul and E. Barkan MICCAI DLMIA workshop, 2016 2016 Medical Imaging 
A Cross Saliency Approach to Asymmetry Based Tumor Detection M. Erichov, S. Alpert ,P. Kisilev and S. Hashoul MICCAI 2015 2015 Medical Imaging 
Automatic Dual-View Mass Detection in Full-Field Digital Mammograms, G. Amit, S. Hashoul, P. Kisilev, B. Ophir, E. Walach, A. Zlotnick MICCAI 2015 2015 Medical Imaging 
Shape from Focus with Adaptive Focus Measure and High Order Derivatives Y. Frommer, R. Ben-Ari and N. Kiryati BMVC 2015 2015 Medical Imaging 
Semantic Description of Medical Image Findings: Structured Learning Approach P. Kisilev, E. Walach, S. Hashoul, E. Barkan, B. Ophir and S. Alpert BMVC 2015 2015 Medical Imaging 
Computational Mammography using Deep Neural Networks, Deep Learning Workshop A. Dubrovina, P. Kisilev, B. Ginsburg, S. Hashoul, and R. Kimmel MICCAI 2015 2015 Medical Imaging 
Hybrid Unsupervised-Supervised Lesion Detection in Mammograms A. Zlotnick, B. Ophir and P. Kisilev SPIE MI 2015 2015 Medical Imaging 
From Medical Image to Automatic Medical Report Generation P. Kisilev, E. Walach, E. Barkan, B. Ophir, S. Alpert and S. Hashoul IBM Research Journal 2015 Medical Imaging 
Automated Planning of Breast Radiotherapy using Cone Beam CT Imaging G. Amit and T. G. Purdie Medical Physics 42(2), 770-779, 2015 2015 Medical Imaging 
Automatic learning-based beam angle selection for thoracic IMRT G. Amit, T. G. Purdie, A. Levinshtein, A. J. Hope, P. Lindsay, A. Marshall, D. A Jaffray and V. Pekar Medical Physics 42(4), 1992-2005, 2015 2015 Medical Imaging 
Self-contained Information Retention Format (SIRF) S. Rabinovici-Cohen, M. Baker, R. Cumming, S. Fineberg and P. Viana SNIA Public Review 2015 2015 Medical Imaging 
Image Classification using Clinical and Visual Data Fusion by Multiple Kernel Learning P. Kisilev, S. Hashoul, E. Walach and A. Tzadok SPIE MI, 2014 2014 Medical Imaging 
Unsupervised Detection of Abnormalities in Medical Images using Salient Features S. Alpert and P. Kisilev SPIE MI, 2014 2014 Medical Imaging 
Storlet Engine for Executing Biomedical Processes within the Storage System S. Rabinovici-Cohen, E. Henis, J. Marberg and K. Nagin Proceedings of the 7th International Workshop on Process-oriented Information Systems in Healthcare (ProHealth), Eindhoven, the Netherlands, 2014 2014 Medical Imaging 
Self-contained Information Retention Format for Future Semantic Interoperability S. Rabinovici-Cohen, R. Cummings, S. Fineberg Proceedings of the 4th International Workshop on Semantic Digital Archives (SDA), London, UK, 2014 2014 Medical Imaging 
A Fully Automatic Lesion Classification in Breast Ultrasound E. Walach, P. Kisilev, D. Chevion, E. Barkan, S. Harary, S. Hashoul, A. Ben-Horesh and A. Tzadok MICCAI BIA, 2013 2013 Medical Imaging 
Learning to Detect Lesion Boundaries in Breast Ultrasound Images P. Kisilev, E. Barkan, G. Shakhnarovich and A. Tzadok MICCAI BIA, 2013 2013 Medical Imaging 
DFlow and DField: New features for capturing object and image relationships P. Kisilev, D. Freedman, E. Walach and A. Tzadok In Proceeedings of the International Conference on Pattern Recognition (ICPR), 2012 2012 Medical Imaging 
Quantitative Tissue Characterization Based On Pulsed-Echo Ultrasound Scans E. Walach, C.N. Liu,R.C. Waag, and J. Parker IEEE Transactions on Biomedical Engineering, vol. BME-33, no. 7, pp. 637-643, July 1986 1986 Medical Imaging 
Local Tissue Attenuation Images Based On Pulsed-Echo Ultrasound Scans E. Walach, A. Shmulewitz, Y. Itzchak, and Z. Heyman IEEE Transactions on Biomedical Engineering, vol. BME-36, no. 2, pp. 211-221, February 1989 1989 Medical Imaging 
Ultrasonic Attenuation Maps of Liver Based on a Conventional B-Scans and an Amplitude Loss Technique Estimates A. Shmulewitz, Z. Heyman, E. Walach, B. Ramot, and Y. Itzchak, Investigative Radiology, vol.25 No. 10, 1990 1990 Medical Imaging 
Quantitative Estimation of Attenation in Ultrasound Video Images: Correlation with Histology in Diffuse Liver Diseases M. Graif, M. Yanuka, M. Baraz, A. Blank, M. Moshkovitz, A. Kessler, T. Gilat, E. Walach, P. Amazeen and C. Irving Investigative Radiology, 2000 2000 Medical Imaging 
Revolutionary impact of XML on biomedical information interoperability" A. Shabo, S. Rabinovici-Cohen, P. Vortman: IBM Systems Journal 45(2): 361-372, 2006 2006 Medical Imaging 
Data mining and clinical data repositories: Insights from a 667,000 patient data set I M Mullins, M S Siadaty, J Lyman, K Scully, C T Garrett, W Greg Miller, R Muller, B Robson, C Apte, S Weiss, I Rigoutsos, D Platt, S Cohen, W Knaus Computers in biology and medicine 36(12), 1351--1377, 2006 2006 Medical Imaging 
Computer-aided simple triage (CAST) for coronary CT angiography (CCTA) R. Goldenberg, D. Eilot, G. Begelman, E. Walach, E. Ben-Ishai and N. Peled Int. J. Computer Assisted Radiology and Surgery, 2012 2012 Medical Imaging 
A New Approach to Display of Ultrasound Data Y.Itzchak, Z. Heyman, E. Walach, R. Hilgendorf Proceedings of the conference on "The Leading Edge in Diagnostic Ultrasound", Atlantic City, USA, May 1986 1986 Medical Imaging 
Two-dimensional Ultrasonic Attenuation Maps of Normal Liver in Vivo Y. Itzchak, Z. Heyman, and E. Walach 22 Annual Conference of Radiological Soc. of North America, Chicago, USA, November 1986 1986 Medical Imaging 
Two-dimensional Attenuation Map of the Liver in Lymphoma and Hodgkins Diseases Y. Itzchak, Z. Heyman, E. Walach, and B. Ramot 22 Annual Conference of Radiological Soc. of North America, Chicago, USA, November 1986 1986 Medical Imaging 
Local Attenuation Mapping Based on Pulsed-Echo Ultrasound Scans E. Walach, Y. Itzchak, and Z. Heyman The 15th conference of EE Engineers in Israel, Tel-Aviv, Israel, pp., April 7-9, 1987 1987 Medical Imaging 
Dual Color Representation of Attenuation and Reflection of Ultrasound Scans A. Shmulewitz, Y. Itzchak, E. Walach, and Z. Heyman 23 Annual Conference of Radiological Soc. of North America, Chicago, USA, December 1987 1987 Medical Imaging 
Wavelet Representation and Total Variation Regularization in Emission Tomography P. Kisilev, M. Zibulevsky, Y. Y. Zeevi in Proc. ICIP conf., Thessaloniki, 2001 2001 Medical Imaging 
Wavelet Transform Based Maximum Likelihood Reconstruction in Emission Tomography P. Kisilev, M. Jacobson, Y. Y. Zeevi in Proc. World Congress on Biomedical Engineering, Chicago, 2000 2000 Medical Imaging 
Estimation of Noisy Signals Based on Local Transforms P. Kisilev, Y. Y. Zeevi in Proc. Signal Processing Workshop, Utah, 1998 1998 Medical Imaging 
Detection and Processing of Single Trial Evoked Potentials P. Kisilev, Y. Y. Zeevi, H. Pratt in Proc. IEEE conf. on Biomedical Sig. Proc., Greece 1997 1997 Medical Imaging 
Detection of Incidental Pulmonary Nodules in Coronary CTA – The Potential Role of Computer Aided Detection G. Bartal, J. M. Gomori, E. Rivlin, R. Goldenberg, E. Wallach, U. Soimu, N. Peled RSNA, Chicago, November, 2003 2003 Medical Imaging 
Identification of Malignant Breast Tumors Based on Acoustic Attenuation Mapping of Conventional Ultrasound Images S. Harray, E. Walach 233-243, MICCAI, Nice, France, 2012 2012 Medical Imaging 
Biomedical information integration middleware for clinical genomics S. Rabinovici-Cohen Proceedings of Next Generation Information Technologies and Systems (NGITS), Haifa, Israel, June 2009 2009 Medical Imaging 
PACS and Electronic Health Records" S. Cohen, Flora Gilboa, Uri Shani in Proc. SPIE Medical Imaging, Feb. 2002 2002 Medical Imaging