Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training
paradigm, successfully introduces text supervision to vision models. It has shown promising …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …

Interactive segmentation is a crucial research area in medical image analysis aiming to
boost the efficiency of costly annotations by incorporating human feedback. This feedback …

Tumor synthesis enables the creation of artificial tumors in medical images facilitating the
training of AI models for tumor detection and segmentation. However success in tumor …

The ability to dynamically extend a model to new data and classes is critical for multiple
organ and tumor segmentation. However, due to privacy regulations, accessing previous …

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 …

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-
dimensional CT volumes sourced from 112 hospitals across diverse populations …

The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a
wave of opportunities in computational healthcare. The interactive nature of these models …

AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and
low prevalence of early tumors. Tumor synthesis seeks to create artificial tumors in medical …