Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range
of downstream tasks have gained significant interest lately in various deep-learning …

Foundation model, trained on a diverse range of data and adaptable to a myriad of tasks, is
advancing healthcare. It fosters the development of healthcare artificial intelligence (AI) …

Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

Medical image analysis (MedIA) has become an essential tool in medicine and healthcare,
aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in …

Recent advancements in Artificial Intelligence (AI) have significantly influenced the field of
Cardiovascular Disease (CVD) analysis, particularly in image-based diagnostics. Our paper …

Harnessing the power of pre-training on large-scale datasets like ImageNet forms a
fundamental building block for the progress of representation learning-driven solutions in …

Increasing the throughput of the Transformer architecture, a foundational component used in
numerous state-of-the-art models for vision and language tasks (eg, GPT, LLaVa), is an …

This paper tackles a novel yet challenging problem: how to transfer knowledge from the
emerging Segment Anything Model (SAM)--which reveals impressive zero-shot instance …

By integrating visual and linguistic understanding, visual–language foundation models
(VLFMs) have the great potential to advance the interpretation of medical data, thereby …

State-of-the-art medical multi-modal large language models (med-MLLM), like LLaVA-Med
or BioMedGPT, leverage instruction-following data in pre-training. However, those models …