The transformer is primarily used in the field of natural language processing. Recently, it has
been adopted and shows promise in the computer vision (CV) field. Medical image analysis …

Medical image segmentation plays a crucial role in various healthcare applications,
enabling accurate diagnosis, treatment planning, and disease monitoring. Traditionally …

Medical image segmentation serves as a crucial underpinning for a myriad of clinical
applications. The advent of deep learning techniques has significantly propelled …

Due to the lack of quality annotation in medical imaging community, semi-supervised
learning methods are highly valued in image semantic segmentation tasks. In this paper, an …

Textual backdoor attacks pose significant security threats. Current detection approaches,
typically relying on intermediate feature representation or reconstructing potential triggers …

Medical image analysis has benefited from deep learning techniques not only because of
network architecture engineering, but also a large number of high-quality annotations which …

Deep learning has driven remarkable advancements in medical image segmentation. The
requirement for comprehensive annotations, however, poses a significant challenge due to …

Image semantic segmentation is a dense prediction task in computer vision that is
dominated by deep learning techniques in recent years. UNet, which is a symmetric encoder …

Annotating a large amount of medical imaging data thoroughly for training purposes can be
expensive, particularly for medical image segmentation tasks. Instead, obtaining less …

Deep learning models, eg supervised Encoder-Decoder style networks, exhibit promising
performance in medical image segmentation, but come with a high labelling cost. We …