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 …

Transformer, an attention-based encoder–decoder model, has already revolutionized the
field of natural language processing (NLP). Inspired by such significant achievements, some …

Brain tumors have shown extreme mortality and increasing incidence during recent years,
which bring enormous challenges for the timely diagnosis and effective treatment of brain …

The missing modality issue is critical but non-trivial to be solved by multi-modal models.
Current methods aiming to handle the missing modality problem in multi-modal tasks, either …

Automatic segmentation of medical images plays an important role in the diagnosis of
diseases. On single-modal data, convolutional neural networks have demonstrated …

Multi-modal medical image segmentation is a crucial task in oncology that enables the
precise localization and quantification of tumors. The aim of this work is to present a meta …

In this study, we focus on building up a model that can Segment Anything in medical
scenarios, driven by Text prompts, termed as SAT. Our main contributions are three folds:(i) …

Radiologists encounter significant challenges when segmenting and determining brain
tumors in patients because this information assists in treatment planning. The utilization of …

In medical vision, different imaging modalities provide complementary information. However,
in practice, not all modalities may be available during inference or even training. Previous …

Multimodal learning has shown great potentials in numerous scenes and attracts increasing
interest recently. However, it often encounters the problem of missing modality data and thus …