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MyTH
PublicThis code implements MyTH, an approach for privacy-preserving and visually interpretable identification of data bias in federated learning for images (the paper is under review in npj Digital Medicine). Authors: Klavdiia Naumova (EPFL), Arnout Devos (ETH), Sai Praneeth Karimireddy (UC Berkeley, USC), Martin Jaggi (EPFL), Mary-Anne Hartley (Yale)MultiModN
Publiccumulator
PublicDeepBreath-NatMed23
PublicMoDN-ML4H
PublicWhat-If_model
PubliccoML-Personalized-v2
PublicCS433-2021-ecoML
PublicCS433-2021-eCARE1
Publicwhat-if_platform_backend
PublicHartley2020-PLoSMedicine
Public