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 …

Annotating medical images, particularly for organ segmentation, is laborious and time-
consuming. For example, annotating an abdominal organ requires an estimated rate of 30 …

The Segment Anything Model (SAM) has recently emerged as a groundbreaking model in
the field of image segmentation. Nevertheless, both the original SAM and its medical …

This paper presents the challenge report for the 2021 Kidney and Kidney Tumor
Segmentation Challenge (KiTS21) held in conjunction with the 2021 international …

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 …

Prior to the deep learning era, shape was commonly used to describe the objects.
Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly …

Self-supervised learning (SSL) has recently achieved promising performance for 3D medical
image analysis tasks. Most current methods follow existing SSL paradigm originally …