Search Results for author: Andreanne Lemay

Found 10 papers, 5 papers with code

Improving the repeatability of deep learning models with Monte Carlo dropout

1 code implementation15 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.

Attribute Binary Classification +6

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.


Monte Carlo dropout increases model repeatability

1 code implementation12 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.

Classification Density Estimation

Evaluating subgroup disparity using epistemic uncertainty in mammography

no code implementations6 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.

BIG-bench Machine Learning Decision Making +1

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.

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

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