429 citations · 730 across the 4 of their papers we have counts for
4 papers
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Abel Díaz Berenguer, Hichem Sahli, Boris Joukovsky +37
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 c…
Optimization for Medical Image Segmentation: Theory and Practice when evaluating with Dice Score or Jaccard Index
Tom Eelbode, Jeroen Bertels, Maxim Berman +4
In many medical imaging and classical computer vision tasks, the Dice score and Jaccard index are used to evaluate the segmentation performance. Despite the existence and great emp…
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
Sofie Tilborghs, Ine Dirks, Lucas Fidon +19
Recent research on COVID-19 suggests that CT imaging provides useful information to assess disease progression and assist diagnosis, in addition to help understanding the disease.…
Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice
Jeroen Bertels, Tom Eelbode, Maxim Berman +4
The Dice score and Jaccard index are commonly used metrics for the evaluation of segmentation tasks in medical imaging. Convolutional neural networks trained for image segmentation…