2 citations · 2 across the 4 of their papers we have counts for
4 papers
Unsupervised Adaptation of 3D CT Foundation Models for 3D CBCT Segmentation
Gauthier Miralles, Loic Le Folgoc, Vincent Jugnon +1
Accurate 3D segmentation of cone-beam CT (CBCT) is critical for interventional and radiation therapy applications, yet it remains limited by two compounding challenges: the scarcit…
Unsupervised Domain Adaptation with Target-Only Margin Disparity Discrepancy
Gauthier Miralles, Loïc Le Folgoc, Vincent Jugnon +1
In interventional radiology, Cone-Beam Computed Tomography (CBCT) is a helpful imaging modality that provides guidance to practicians during minimally invasive procedures. CBCT dif…
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…
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…