Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with
applications in disease diagnostics, intervention, and treatment planning. Accurate MRI …

We propose a novel framework for retinal feature point alignment, designed for learning
cross-modality features to enhance matching and registration across multi-modality retinal …

Applying pre-trained medical segmentation models on out-of-domain images often yields
predictions of insufficient quality. Several strategies have been proposed to maintain model …

Test-time adaptation (TTA) aims to provide neural networks capable of adapting to the target
domain distribution using only unlabeled test data. Most existing TTA methods have …

In the field of histopathology, computer-aided systems face significant challenges due to
diverse domain shifts. They include variations in tissue source organ, preparation and …

Convolutional Neural Networks (CNNs) exhibit strong performance in medical image
segmentation tasks by capturing high-level (local) information, such as edges and textures …

Soft tissues sarcomas include striated muscle, fibrous tissue, fat, and other soft tissues.
Simultaneously, their mortality rates are comparable to those of esophageal cancer, cervical …