17 citations · 23 across the 5 of their papers we have counts for
6 papers
A foundation for systematic analysis of transformers and RNNs for tractography
Emmanuelle Renauld, Philippe Poulin, Hugo Larochelle +2
Machine learning (ML) has emerged as a promising approach for improving diffusion MRI (dMRI) tractography, a task that remains limited by the intrinsic tension between local diffus…
Exploring the robustness of TractOracle methods in RL-based tractography
Jeremi Levesque, Antoine Théberge, Maxime Descoteaux +1
Tractography algorithms leverage diffusion MRI to reconstruct the fibrous architecture of the brain's white matter. Among machine learning approaches, reinforcement learning (RL) h…
TractOracle: towards an anatomically-informed reward function for RL-based tractography
Antoine Théberge, Maxime Descoteaux, Pierre-Marc Jodoin
Reinforcement learning (RL)-based tractography is a competitive alternative to machine learning and classical tractography algorithms due to its high anatomical accuracy obtained w…
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…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Manifold-aware Synthesis of High-resolution Diffusion from Structural Imaging
Benoit Anctil-Robitaille, Antoine Théberge, Pierre-Marc Jodoin +3
The physical and clinical constraints surrounding diffusion-weighted imaging (DWI) often limit the spatial resolution of the produced images to voxels up to 8 times larger than tho…