Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Background Medicine is inherently multimodal, requiring the simultaneous interpretation
and integration of insights between many data modalities spanning text, imaging, genomics …

Training segmentation models for medical images continues to be challenging due to the
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Rapid treatment protocols such as X-ray and CT scans have played a crucial role in the
diagnosis of tuberculosis (TB infection). Automatic detection of CXR is required to speed up …

In this study, we aim to initiate the development of Radiology Foundation Model, termed as
RadFM. We consider the construction of foundational models from three perspectives …

Deep neural networks have been integrated into the whole clinical decision procedure
which can improve the efficiency of diagnosis and alleviate the heavy workload of …

Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or
modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize …

Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …