Search Results for author:
Found 9 papers, 5 papers with code
The Dice loss in the context of missing or empty labels: Introducing $Φ$ and $ε$
We find and propose heuristic combinations of $\Phi$ and $\epsilon$ that work in a segmentation setting with either missing or empty labels.
Shape constrained CNN for segmentation guided prediction of myocardial shape and pose parameters in cardiac MRI
We show the benefits of simultaneous semantic segmentation and the two newly defined loss functions for the prediction of shape parameters.
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
Specifically, we propose a linearly scalable approach to context modeling, formulating Nonnegative Matrix Factorization (NMF) as a differentiable layer integrated into a U-shaped architecture.
On the relationship between calibrated predictors and unbiased volume estimation
This has led to a renewed focus on calibrated predictions in the medical imaging and broader machine learning communities.
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding.
Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index
We verify these results empirically in an extensive validation on six medical segmentation tasks and can confirm that metric-sensitive losses are superior to cross-entropy based loss functions in case of evaluation with Dice Score or Jaccard Index.
Shape Constrained CNN for Cardiac MR Segmentation with Simultaneous Prediction of Shape and Pose Parameters
We evaluated the proposed method in a fivefold cross validation on a in-house clinical dataset with 75 subjects containing a total of 1539 delineated short-axis slices covering LV from apex to base, and achieved a correlation of 99% for LV area, 94% for myocardial area, 98% for LV dimensions and 88% for regional wall thicknesses.
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans.
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice
First, we investigate the theoretical differences in a risk minimization framework and question the existence of a weighted cross-entropy loss with weights theoretically optimized to surrogate Dice or Jaccard.
