Our method builds upon previous Medical Out-of-Distribution (MOOD) chall...
Unsupervised Out-of-Distribution (OOD) detection consists in identifying...
NeSy4VRD is a multifaceted resource designed to support the development ...
U-Net has been the go-to architecture for medical image segmentation tas...
We propose a novel unsupervised out-of-distribution detection method for...
We propose an out-of-distribution detection method that combines density...
We introduce the novel concept of anti-transfer learning for neural netw...
Deep learning has become the most widely used approach for cardiac image...
In this work, we present a fully automatic method to segment cardiac
str...
Cardiac MR image segmentation is essential for the morphological and
fun...
In the recent years, convolutional neural networks have transformed the ...
Quantification of anatomical shape changes still relies on scalar global...
Accurate segmentation of heart structures imaged by cardiac MR is key fo...
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image sta...
Segmentation of image sequences is an important task in medical image
an...
Alterations in the geometry and function of the heart define well-establ...
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depe...
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging
mo...
The availability of training data for supervision is a frequently encoun...