This article discusses the opportunities, applications and future directions of large-scale
pretrained models, ie, foundation models, which promise to significantly improve the …

In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

Quantitative organ assessment is an essential step in automated abdominal disease
diagnosis and treatment planning. Artificial intelligence (AI) has shown great potential to …

Recent advancements in foundation models have shown significant potential in medical
image analysis. However, there is still a gap in models specifically designed for medical …

Transformers for medical image segmentation have attracted broad interest. Unlike
convolutional networks (CNNs), transformers use self-attentions that do not have a strong …

Biomedical image segmentation is a crucial part of both scientific research and clinical care.
With enough labelled data, deep learning models can be trained to accurately automate …

We described a challenge named" DRAC-Diabetic Retinopathy Analysis Challenge" in
conjunction with the 25th International Conference on Medical Image Computing and …

Abstract Large Vision-Language Models (LVLMs) are capable of handling diverse data
types such as imaging, text, and physiological signals, and can be applied in various fields …

Open international challenges are becoming the de facto standard for assessing computer
vision and image analysis algorithms. In recent years, new methods have extended the …