13 citations · 33 across the 7 of their papers we have counts for
6 papers · 1 filter
Improving the repeatability of deep learning models with Monte Carlo dropout
Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge +7
The integration of artificial intelligence into clinical workflows requires reliable and robust models. Repeatability is a key attribute of model robustness. Repeatable models outp…
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…
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…
Kidney Recognition in CT Using YOLOv3
Andréanne Lemay
Organ localization can be challenging considering the heterogeneity of medical images and the biological diversity from one individual to another. The contribution of this paper is…