3 papers
cs.LG2026
Evaluating Federated Learning approaches for mammography under breast density heterogeneity
Gonzalo Iñaki Quintana, Franco Martin Di Maria, Laurence Vancamberg
Breast density is a key factor that influences mammography interpretation and is a major source of heterogeneity in multicenter datasets. Such heterogeneity poses challenges for co…
cs.LG2025
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application
Gonzalo Iñaki Quintana, Laurence Vancamberg, Vincent Jugnon +2
This work studies the relationship between Contrastive Learning and Domain Adaptation from a theoretical perspective. The two standard contrastive losses, NT-Xent loss (Self-superv…
cs.LG2024
BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning
Gonzalo Iñaki Quintana, Laurence Vancamberg, Vincent Jugnon +2
Federated Learning (FL) is gaining traction as a learning paradigm for training Machine Learning (ML) models in a decentralized way. Batch Normalization (BN) is ubiquitous in Deep…