The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

Foundation models (FMs) are large-scale deeplearning models that are developed using
large datasets and self-supervised learning methods. These models serve as a base for …

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

In the past decade, there has been a recognized need for innovative methods to monitor and
manage plant diseases, aiming to meet the precision demands of modern agriculture. Over …

Pathology plays a crucial role in diagnosing and evaluating patient tissue samples obtained
via surgeries and biopsies. The advent of whole slide scanners and the development of …

The Vision Transformer (ViT) has demonstrated remarkable performance in Self-Supervised
Learning (SSL) for 3D medical image analysis. Masked AutoEncoder (MAE) for feature pre …

Quantitative evaluation of echocardiography is essential for precise assessment of cardiac
condition, monitoring disease progression, and guiding treatment decisions. The diverse …

The scarcity of annotations poses a significant challenge in medical image analysis. Large-
scale pre-training has emerged as a promising label-efficient solution, owing to the …

Dense contrastive representation learning (DCRL) has greatly improved the learning
efficiency for image dense prediction tasks, showing its great potential to reduce the large …

Diabetic retinopathy (DR) is a leading cause of preventable blindness and has emerged as
a global health challenge, necessitating the development of robust management strategies …