Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions

ArXi:2604.00397v1 Announce Type: cross Background: Deep learning has nstrated significant potential for automated brain metastases (BM) segmentation; however, models trained at a singular institution often exhibit suboptimal performance at various sites due to disparities in scanner hardware, imaging protocols, and patient graphics. The goal of this work is to create a domain adaptation framework that will allow for BM segmentation to be used across multiple institutions.