7 citations · 19 across the 23 of their papers we have counts for
46 papers
MoP-CLIP: A Mixture of Prompt-Tuned CLIP Models for Domain Incremental Learning
Julien Nicolas, Florent Chiaroni, Imtiaz Ziko +3
Despite the recent progress in incremental learning, addressing catastrophic forgetting under distributional drift is still an open and important problem. Indeed, while state-of-th…
Mixup-Privacy: A simple yet effective approach for privacy-preserving segmentation
Bach Kim, Jose Dolz, Pierre-Marc Jodoin +1
Privacy protection in medical data is a legitimate obstacle for centralized machine learning applications. Here, we propose a client-server image segmentation system which allows f…
What Matters in Reinforcement Learning for Tractography
Antoine Théberge, Christian Desrosiers, Maxime Descoteaux +1
Recently, deep reinforcement learning (RL) has been proposed to learn the tractography procedure and train agents to reconstruct the structure of the white matter without manually…
TFS-ViT: Token-Level Feature Stylization for Domain Generalization
Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri +4
Standard deep learning models such as convolutional neural networks (CNNs) lack the ability of generalizing to domains which have not been seen during training. This problem is mai…
Harmonizing Flows: Unsupervised MR harmonization based on normalizing flows
Farzad Beizaee, Christian Desrosiers, Gregory A. Lodygensky +1
In this paper, we propose an unsupervised framework based on normalizing flows that harmonizes MR images to mimic the distribution of the source domain. The proposed framework cons…
TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation
Mélanie Gaillochet, Christian Desrosiers, Hervé Lombaert
Deep learning methods typically depend on the availability of labeled data, which is expensive and time-consuming to obtain. Active learning addresses such effort by prioritizing w…