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 …

Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

Federated learning allows distributed medical institutions to collaboratively learn a shared
prediction model with privacy protection. While at clinical deployment, the models trained in …

Medical image analysis plays an important role in clinical diagnosis. In this paper, we
examine the recent Segment Anything Model (SAM) on medical images, and report both …

Despite the remarkable performance that modern deep neural networks have achieved on
independent and identically distributed (IID) data, they can crash under distribution shifts …

Diabetic retinopathy (DR) is the most common cause of blindness in middle-aged people. It
shows that an automatic image evaluation system is needed in the diagnosis of this disease …

For medical image segmentation, imagine if a model was only trained using MR images in
source domain, how about its performance to directly segment CT images in target domain …

Single-source domain generalization (SDG) in medical image segmentation is a challenging
yet essential task as domain shifts are quite common among clinical image datasets …

Fundus images have been widely used in routine examinations of ophthalmic diseases. For
some diseases, the pathological changes mainly occur around the optic disc area; therefore …

For medical image analysis, segmentation models trained on one or several domains lack
generalization ability to unseen domains due to discrepancies between different data …