Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation

arXiv:2405.10870v1 Announce Type: cross
Abstract: Objectives: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without forgetting (LWF), to improve model generalizability without sharing raw data.
Materials and methods: A total of six BM datasets from University Hospital Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, NYU and BraTS Challenge 2023 on BM segmentation were used for this …

abstract arxiv brain cs.cv data deep learning eess.iv explore impact incremental materials performance privacy raw raw data training transfer transfer learning type work