The rapid development of artificial intelligence has constantly reshaped the field of
intelligent healthcare and medicine. As a vital technology, multimodal learning has …

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

Computational pathology, has witnessed considerable progress in the development of both
task-specific predictive models and task-agnostic self-supervised vision encoders …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …

Large Language Models (LLMs) have rapidly evolved from text-based systems to
multimodal platforms, significantly impacting various sectors including healthcare. This …

Conventional task-and modality-specific artificial intelligence (AI) models are inflexible in
real-world deployment and maintenance for biomedicine. At the same time, the growing …

Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis
comprises interdependent subtasks such as segmentation, detection and recognition, which …

Automated medical image analysis systems often require large amounts of training data with
high quality labels, which are difficult and time consuming to generate. This paper …

This paper introduces MedTrinity-25M, a comprehensive, large-scale multimodal dataset for
medicine, covering over 25 million images across 10 modalities, with multigranular …

Multi-Modal Large Language Models (MLLMs), despite being successful, exhibit limited
generality and often fall short when compared to specialized models. Recently, LLM-based …