7 citations · 21 across the 9 of their papers we have counts for
10 papers
Label fusion and training methods for reliable representation of inter-rater uncertainty
Andreanne Lemay, Charley Gros, Enamundram Naga Karthik +1
Medical tasks are prone to inter-rater variability due to multiple factors such as image quality, professional experience and training, or guideline clarity. Training deep learning…
2D Multi-Class Model for Gray and White Matter Segmentation of the Cervical Spinal Cord at 7T
Nilser J. Laines Medina, Charley Gros, Julien Cohen-Adad +2
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 (…
Team NeuroPoly: Description of the Pipelines for the MICCAI 2021 MS New Lesions Segmentation Challenge
Uzay Macar, Enamundram Naga Karthik, Charley Gros +2
This paper gives a detailed description of the pipelines used for the 2nd edition of the MICCAI 2021 Challenge on Multiple Sclerosis Lesion Segmentation. An overview of the data pr…
Impact of individual rater style on deep learning uncertainty in medical imaging segmentation
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 impac…
Benefits of Linear Conditioning with Metadata for Image Segmentation
Andreanne Lemay, Charley Gros, Olivier Vincent +3
Medical images are often accompanied by metadata describing the image (vendor, acquisition parameters) and the patient (disease type or severity, demographics, genomics). This meta…
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning
Andreanne Lemay, Charley Gros, Zhizheng Zhuo +4
Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can…
SoftSeg: Advantages of soft versus binary training for image segmentation
Charley Gros, Andreanne Lemay, Julien Cohen-Adad
Most image segmentation algorithms are trained on binary masks formulated as a classification task per pixel. However, in applications such as medical imaging, this "black-and-whit…
ivadomed: A Medical Imaging Deep Learning Toolbox
Charley Gros, Andreanne Lemay, Olivier Vincent +4
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data. The package includes APIs, comman…
Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?
Olivier Vincent, Charley Gros, Joseph Paul Cohen +1
Despite recent improvements in medical image segmentation, the ability to generalize across imaging contrasts remains an open issue. To tackle this challenge, we implement Feature-…
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
Charley Gros, Benjamin De Leener, Atef Badji +49
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of…