[2604.00397] Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.00397 (cs) [Submitted on 1 Apr 2026] Title:Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions Authors:Yuchen Yang, Shuangyang Zhong, Haijun Yu, Langcuomu Suo, Hongbin Han, Florian Putz, Yixing Huang View a PDF of the paper titled Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions, by Yuchen Yang and 6 other authors View PDF HTML (experimental) Abstract:Background: Deep learning has demonstrated 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 demographics. The goal of this work is to create a domain adaptation framework that will allow for BM segmentation to be used across multiple institutions. Methods: We propose a VAE-MMD preprocessing pipeline that combines variational autoencoders (VAE) with maximum mean discrepancy (MMD) loss, incorporating skip connections and self-attention mechanisms alongside nnU-Net segmentation. The method was tested on 740 patients from four public databases: Stanford, UCSF, UCLM, and PKG, evaluated by domain classifier's accuracy, sensitivity, precision, F1/F2 scores, surface Dice (sDice), and 95th percentile Hausdorff distance (HD95). Results: VAE-MMD reduced domain classifier accuracy from...