Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …

Abstract Model generalization capacity at domain shift (eg, various imaging protocols and
scanners) is crucial for deep learning methods in real-world clinical deployment. This paper …

Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With
the pressures for improved care and translation of the latest medical advances and …

Deep convolutional neural networks have significantly boosted the performance of fundus
image segmentation when test datasets have the same distribution as the training datasets …

With the success of the 3D deep learning models, various perception technologies for
autonomous driving have been developed in the LiDAR domain. While these models …

In recent years, several deep learning models have been proposed to accurately quantify
and diagnose cardiac pathologies. These automated tools heavily rely on the accurate …

Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a
set of normal training samples to identify abnormal samples in test data. Most existing AD …

Convolutional neural networks have been widely applied to medical image segmentation
and have achieved considerable performance. However, the performance may be …