Data drift refers to differences between the data used in training a machine learning (ML)
model and that applied to the model in real-world operation. Medical ML systems can be …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Semi-supervised learning (SSL) has recently demonstrated great success in medical image
segmentation, significantly enhancing data efficiency with limited annotations. However …

Deep learning-based medical image segmentation models often suffer from domain shift,
where the models trained on a source domain do not generalize well to other unseen …

Deep learning has achieved tremendous success with independent and identically
distributed (iid) data. However, the performance of neural networks often degenerates …

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 …

Addressing fairness in artificial intelligence (AI), particularly in medical AI, is crucial for
ensuring equitable healthcare outcomes. Recent efforts to enhance fairness have …

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 …

Due to the cross-domain distribution shift aroused from diverse medical imaging systems,
many deep learning segmentation methods fail to perform well on unseen data, which limits …