Search Results for author: Charley Gros

Found 10 papers, 5 papers with code

Label fusion and training methods for reliable representation of inter-rater uncertainty

no code implementations15 Feb 2022 Andreanne Lemay, Charley Gros, Enamundram Naga Karthik, Julien Cohen-Adad

Each label fusion method is studied using both the conventional training framework and the recently published SoftSeg framework that limits information loss by treating the segmentation task as a regression.

2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T

1 code implementation13 Oct 2021 Nilser J. Laines Medina, Charley Gros, Julien Cohen-Adad, Virginie Callot, Arnaud Le Troter

The spinal cord (SC), which conveys information between the brain and the peripheral nervous system, plays a key role in various neurological disorders such as multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS), in which both gray matter (GM) and white matter (WM) may be impaired.

Data Augmentation

Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge

1 code implementation12 Sep 2021 Uzay Macar, Enamundram Naga Karthik, Charley Gros, Andréanne Lemay, Julien Cohen-Adad

This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion Segmentation.

Lesion Segmentation

Impact of individual rater style on deep learning uncertainty in medical imaging segmentation

no code implementations5 May 2021 Olivier Vincent, Charley Gros, Julien Cohen-Adad

While multiple studies have explored the relation between inter-rater variability and deep learning model uncertainty in medical segmentation tasks, little is known about the impact of individual rater style.

Medical Image Segmentation

Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning

no code implementations23 Dec 2020 Andreanne Lemay, Charley Gros, Zhizheng Zhuo, Jie Zhang, Yunyun Duan, Julien Cohen-Adad, Yaou Liu

To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation.

Tumor Segmentation

SoftSeg: Advantages of soft versus binary training for image segmentation

no code implementations18 Nov 2020 Charley Gros, Andreanne Lemay, Julien Cohen-Adad

SoftSeg produces consistent soft predictions at tissues' interfaces and shows an increased sensitivity for small objects.

Binarization Brain Tumor Segmentation +5

ivadomed: A Medical Imaging Deep Learning Toolbox

1 code implementation20 Oct 2020 Charley Gros, Andreanne Lemay, Olivier Vincent, Lucas Rouhier, Anthime Bucquet, Joseph Paul Cohen, Julien Cohen-Adad

ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data.

object-detection Object Detection +1

Cannot find the paper you are looking for? You can Submit a new open access paper.