1 code implementation • 30 May 2023 • Katharina V. Hoebel, Andreanne Lemay, John Peter Campbell, Susan Ostmo, Michael F. Chiang, Christopher P. Bridge, Matthew D. Li, Praveer Singh, Aaron S. Coyner, Jayashree Kalpathy-Cramer
These labels are used to train and evaluate disease severity prediction models.
1 code implementation • 15 Feb 2022 • Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Brian Befano, Silvia de Sanjosé, Diden Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer
During model development and evaluation, much attention is given to classification performance while model repeatability is rarely assessed, leading to the development of models that are unusable in clinical practice.
no code implementations • 15 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.
1 code implementation • 12 Nov 2021 • Andreanne Lemay, Katharina Hoebel, Christopher P. Bridge, Didem Egemen, Ana Cecilia Rodriguez, Mark Schiffman, John Peter Campbell, Jayashree Kalpathy-Cramer
Leveraging Monte Carlo predictions significantly increased repeatability for all tasks on the binary, multi-class, and ordinal models leading to an average reduction of the 95% limits of agreement by 17% points.
1 code implementation • 9 Sep 2021 • Charles Lu, Andreanne Lemay, Ken Chang, Katharina Hoebel, Jayashree Kalpathy-Cramer
Deep learning has the potential to automate many clinically useful tasks in medical imaging.
no code implementations • 6 Jul 2021 • Charles Lu, Andreanne Lemay, Katharina Hoebel, Jayashree Kalpathy-Cramer
As machine learning (ML) continue to be integrated into healthcare systems that affect clinical decision making, new strategies will need to be incorporated in order to effectively detect and evaluate subgroup disparities to ensure accountability and generalizability in clinical workflows.
no code implementations • 18 Feb 2021 • Andreanne Lemay, Charley Gros, Olivier Vincent, Yaou Liu, Joseph Paul Cohen, Julien Cohen-Adad
This metadata is usually disregarded by image segmentation methods.
no code implementations • 23 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.
no code implementations • 18 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.
1 code implementation • 20 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.