3 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
Merging multiple input descriptors and supervisors in a deep neural network for tractogram filtering
Daniel Jörgens, Pierre-Marc Jodoin, Maxime Descoteaux +1
One of the main issues of the current tractography methods is their high false-positive rate. Tractogram filtering is an option to remove false-positive streamlines from tractograp…
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…
Echocardiography Segmentation with Enforced Temporal Consistency
Nathan Painchaud, Nicolas Duchateau, Olivier Bernard +1
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer…
Retrieval in Long Surveillance Videos using User Described Motion and Object Attributes
Greg Castanon, Mohamed Elgharib, Venkatesh Saligrama +1
We present a content-based retrieval method for long surveillance videos both for wide-area (Airborne) as well as near-field imagery (CCTV). Our goal is to retrieve video segments,…