Search Results for author:
Found 10 papers, 5 papers with code
A generalized framework to predict continuous scores from medical ordinal labels
These labels are used to train and evaluate disease severity prediction models.
Improving the repeatability of deep learning models with Monte Carlo dropout
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.
Label fusion and training methods for reliable representation of inter-rater uncertainty
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
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.
Fair Conformal Predictors for Applications in Medical Imaging
Deep learning has the potential to automate many clinically useful tasks in medical imaging.
Evaluating subgroup disparity using epistemic uncertainty in mammography
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.
Benefits of Linear Conditioning with Metadata for Image Segmentation
This metadata is usually disregarded by image segmentation methods.
Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning
To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation.
SoftSeg: Advantages of soft versus binary training for image segmentation
SoftSeg produces consistent soft predictions at tissues' interfaces and shows an increased sensitivity for small objects.
ivadomed: A Medical Imaging Deep Learning Toolbox
ivadomed is an open-source Python package for designing, end-to-end training, and evaluating deep learning models applied to medical imaging data.
