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Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging.

R. Yan, L. Qu, Q. Wei, S. Huang, L. Shen, D. Rubin, L. Xing, and Y. Zhou. IEEE Trans. Medical Imaging, 42 (7): 1932-1943 (2023)

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Daniel L Brunner

Daniel L Freiner

Daniel L Straub

Helmut Rubin

Peter Rubin

Other publications of authors with the same name

Annotation for Information Extraction from Mammography Reports.S. Bozkurt, K. Gülkesen, and D. Rubin. ICIMTH, volume 190 of Studies in Health Technology and Informatics, page 183-185. IOS Press, (2013)Probabilistic visual search for masses within mammography images using deep learning.M. Ertosun, and D. Rubin. BIBM, page 1310-1315. IEEE Computer Society, (2015)Ontology Development for a Pharmacogenetics Knowledge Base.D. Oliver, D. Rubin, J. Stuart, M. Hewett, T. Klein, and R. Altman. Pacific Symposium on Biocomputing, page 65-76. (2002)Network Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease.K. Supekar, V. Menon, D. Rubin, M. Musen, and M. Greicius. PLoS Comput. Biol., (2008)Accounting for data variability in multi-institutional distributed deep learning for medical imaging.N. Balachandar, K. Chang, J. Kalpathy-Cramer, and D. Rubin. J. Am. Medical Informatics Assoc., 27 (5): 700-708 (2020)Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.J. Barker, A. Hoogi, A. Depeursinge, and D. Rubin. Medical Image Anal., (2016)Automating Scoliosis Measurements in Radiographic Studies with Machine Learning: Comparing Artificial Intelligence and Clinical Reports.A. Ha, B. Do, A. Bartret, C. Fang, A. Hsiao, A. Lutz, I. Banerjee, G. Riley, D. Rubin, K. Stevens and 4 other author(s). J. Digit. Imaging, 35 (3): 524-533 (2022)Automatic Staging of Cancer Tumors Using AIM Image Annotations and Ontologies.E. Luque, N. Miranda, D. Rubin, and D. Moreira. J. Digit. Imaging, 33 (2): 287-303 (2020)RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR.Y. Zhou, S. Huang, J. Fries, A. Youssef, T. Amrhein, M. Chang, I. Banerjee, D. Rubin, L. Xing, N. Shah and 1 other author(s). CoRR, (2021)Let's Hope it Works! Inaccurate Supervision of Neural Networks with Incorrect Labels: Application to Epilepsy.F. Dubost, E. Hong, D. Fu, N. Bhaskhar, S. Tang, K. Saab, D. Rubin, J. Dunnmon, and C. Lee-Messer. CoRR, (2020)

BibSonomy

Disambiguation of "Rubin, Daniel L."

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Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging.

Please choose a person to relate this publication to

Daniel L Brunner

Daniel L Freiner

Daniel L Straub

Helmut Rubin

Peter Rubin

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Rubin, Daniel L."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging.

Please choose a person to relate this publication to

Daniel L Brunner

Daniel L Freiner

Daniel L Straub

Helmut Rubin

Peter Rubin

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging.