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

Abstract Domain adaptation (DA) has drawn high interest for its capacity to adapt a model
trained on labeled source data to perform well on unlabeled or weakly labeled target data …

Convolutional neural networks (CNNs) have proved to be powerful medical image
segmentation models. In this study, we address some of the main unresolved issues …

Deep learning (DL) has shown remarkable success in various medical imaging data
analysis applications. However, it remains challenging for DL models to achieve good …

Abstract 3D human pose estimation is a key component of clinical monitoring systems. The
clinical applicability of deep pose estimation models, however, is limited by their poor …

Automated pulmonary nodule detection using computed tomography scans is vital in the
early diagnosis of lung cancer. Although extensive well-performed methods have been …

Current unsupervised anomaly localization approaches rely on generative models to learn
the distribution of normal images, which is later used to identify potential anomalous regions …

Supervised learning is well-known to fail at generalization under distribution shifts. In typical
clinical settings, the source data is inaccessible and the target distribution is represented …

Continual test-time adaptation (CTTA) aims to continuously adapt a source-trained model to
a target domain with minimal performance loss while assuming no access to the source …

Multiple instance learning (MIL) deals with data grouped into bags of instances, of which
only the global information is known. In recent years, this weakly supervised learning …