Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

This review article builds upon the introductory piece in our seven-part series, delving
deeper into the transformative potential of generative artificial intelligence (Gen AI) in …

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) …

Current fundus image analysis models are predominantly built for specific tasks relying on
individual datasets. The learning process is usually based on data-driven paradigm without …

Expert annotation of 3D medical image for downstream analysis is resource-intensive,
posing challenges in clinical applications. Visual self-supervised learning (vSSL), though …

Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to
the scarcity of labeled data in the medical domain. By leveraging paired/unpaired vision and …

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …

Recent advancements in deep learning have significantly revolutionized the field of clinical
diagnosis and treatment, offering novel approaches to improve diagnostic precision and …

Understanding brain disorders is crucial for accurate clinical diagnosis and treatment.
Recent advances in Multimodal Large Language Models (MLLMs) offer a promising …

Despite the promising performance of convolutional neural networks (CNNs) in brain tumor
diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow …