The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Artificial intelligence and machine learning are driving a paradigm shift in medicine,
promising data-driven, personalized solutions for managing diabetes and the excess …

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 …

The paucity of high-quality medical imaging datasets could be mitigated by machine
learning models that generate compositionally diverse images that faithfully represent …

Foundation vision-language models are currently transforming computer vision, and are on
the rise in medical imaging fueled by their very promising generalization capabilities …

Foundation models in deep learning are characterized by a single large-scale model trained
on vast amounts of data serving as the foundation for various downstream tasks. Foundation …

Digital pathology has transformed the traditional pathology practice of analyzing tissue
under a microscope into a computer vision workflow. Whole slide imaging allows …

evolved separately and have never worked together well. Thus, it is not surprising that
radiologists often work with disjointed system integrations and clashing user interfaces …

We present a comprehensive evaluation of Parameter-Efficient Fine-Tuning (PEFT)
techniques for diverse medical image analysis tasks. PEFT is increasingly exploited as a …

Objective This study aimed to develop an open-source multimodal large language model
(CXR-LLaVA) for interpreting chest X-ray images (CXRs), leveraging recent advances in …