Precise delineation of multiple organs or abnormal regions in the human body from medical
images plays an essential role in computer-aided diagnosis, surgical simulation, image …

Background Multi‐modal learning is widely adopted to learn the latent complementary
information between different modalities in multi‐modal medical image segmentation tasks …

This work aims to address an advanced keypoint detection problem: how to accurately
detect any keypoints in complex real-world scenarios, which involves massive, messy, and …

The two major challenges to deep-learning-based medical image segmentation are multi-
modality and a lack of expert annotations. Existing semi-supervised segmentation models …

Histology analysis of the tumor micro-environment integrated with genomic assays is the
gold standard for most cancers in modern medicine. This paper proposes a Gene-induced …

In the diverse field of medical imaging, automatic segmentation has numerous applications
and must handle a wide variety of input domains, such as different types of Computed …

Objective. Despite advancements in medical imaging technology, the diagnosis and
positioning of lumbar disc diseases still heavily rely on the expertise and experience of …

The colorectal polyps classification is a critical clinical examination. To improve the
classification accuracy, most computer-aided diagnosis algorithms recognize colorectal …

Learning to segment multiple organs from partially labeled datasets can significantly reduce
the burden of manual annotations. However, due to the large domain gap, learning from …

The use of multimodal data in assisted diagnosis and segmentation has emerged as a
prominent area of interest in current research. However, one of the primary challenges is …