With the widespread application of the Transformer architecture in various modalities,
including vision, the technology of large language models is evolving from a single modality …

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

The growing interest in vision-language models (VLMs) has been driven by improvements in
large language models and vision transformers. Despite the abundance of literature on this …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Large Language Models (LLMs) and Large Multimodal Models (LMMs) exhibit impressive
problem-solving skills in many tasks and domains, but their ability in mathematical …

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 …

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

Multimodal Large Language Models (MLLMs) have tremendous potential to improve the
accuracy, availability, and cost-effectiveness of healthcare by providing automated solutions …

Vision language models (VLMs) have experienced rapid advancements through the
integration of large language models (LLMs) with image-text pairs yet they struggle with …

Abstract Large Vision-Language Models (LVLMs) have demonstrated remarkable
capabilities in various multimodal tasks. However their potential in the medical domain …