Search Results for author:
Found 14 papers, 8 papers with code
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels
As a result, we obtain superior Dice scores and model calibration, which supports the wider adoption of DMLs in practice.
DeepVoxNet2: Yet another CNN framework
We know that both the CNN mapping function and the sampling scheme are of paramount importance for CNN-based image analysis.
Convolutional neural networks for medical image segmentation
In this article, we look into some essential aspects of convolutional neural networks (CNNs) with the focus on medical image segmentation.
Final infarct prediction in acute ischemic stroke
This article focuses on the control center of each human body: the brain.
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation.
USE-Evaluator: Performance Metrics for Medical Image Segmentation Models with Uncertain, Small or Empty Reference Annotations
We study how uncertain, small, and empty reference annotations influence the value of metrics for medical image segmentation on an in-house data set regardless of the model.
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.
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.
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.
Optimization with soft Dice can lead to a volumetric bias
Segmentation is a fundamental task in medical image analysis.
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.
