13 citations · 24 across the 4 of their papers we have counts for
5 papers
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…
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…