Universal medical image segmentation via in-context cross-attention.

ABSTRACT: Semantic segmentation is critical in medical image processing, with traditional specialist models facing adaptation challenges to new tasks or distribution shifts. While both generalist pre-trained models and universal segmentation approaches have emerged as solutions, universal methods offer advantages in versatility, sample efficiency, and integration ease into annotation pipelines. We introduce a novel universal segmentation method based on the premise that pre-selecting relevant regions from support sets improves segmentation accuracy. Our approach implements cross-attention between query images and support set images, coupled with an innovative attention up-scaling mechanism that efficiently computes cross-attention on small-scale features with upscaling to higher resolutions. The des

SUBMITTER: Ciusdel CF 

PROVIDER: S-EPMC12685931 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature