1 citations · 2 across the 3 of their papers we have counts for
6 papers
Analysis of Different Losses for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Image colorization aims to add color information to a grayscale image in a realistic way. Recent methods mostly rely on deep learning strategies. While learning to automatically co…
Influence of Color Spaces for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing col…
Patch-based field-of-view matching in multi-modal images for electroporation-based ablations
Luc Lafitte, Rémi Giraud, Cornel Zachiu +6
Various multi-modal imaging sensors are currently involved at different steps of an interventional therapeutic work-flow. Cone beam computed tomography (CBCT), computed tomography…
Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images
Rémi Giraud, Rodrigo Borba Pinheiro, Yannick Berthoumieu
Most of existing superpixel methods are designed to segment standard planar images as pre-processing for computer vision pipelines. Nevertheless, the increasing number of applicati…
AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
Pierrick Coupé, Boris Mansencal, Michaël Clément +5
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.…
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
Pierrick Coupé, Boris Mansencal, Michaël Clément +5
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.…