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.
Open-source projects
Michal Rosen-Zvi,
Director, Healthcare Informatics for IBM Research,
Manager of AI for Healthcare,
IBM Research - Haifa
Latest Awards
- Won 2nd place in the DBTex Challenge to detect biopsy-verified lesion locations in digital breast tomosynthesis.
The team collaborated with NYU on a paper published in Nature Machine Intelligence "Lessons from the First DBTex Challenge" and came in 2nd place in phase 2 of the challenge. - Won 2nd place in the KITS 2021 challenge for kidney and kidney tumor segmentation.
using our solution based on algorithms by Alex Golts, Daniel Khapun, Daniel Shats, Yoel Shoshan, Flora Gilboa-Solomon described in "An Ensemble of 3D U-Net Based Models for Segmentation of Kidney and Masses in CT Scans" which also won Best Paper at the MICCAI workshop. - The team won 1st place at the first Partners HealthCare Biobank Disease Challenge as posted in December 2018.
Publications
| 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 Learning | Selected 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 Learning | Selected 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 Learning | Selected 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 Learning | Selected 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 Imaging | Selected 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 Learning | Selected 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 Learning | Selected 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 Learning | Selected 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 Learning | Selected 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 Imaging | Selected 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 Imaging | Selected 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 Learning | Selected 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 Learning | Selected 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 Imaging | Selected 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. 3.2.1.1-3.2.1.4, 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 |