Federated Learning for Pre-operative Detection of Triple-Negative Breast Cancer from Multiparametric MRI: Preliminary Results

Abstract

Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognosis and limited treatments, for which accurate pre-operative prediction is essential for guiding therapy. While multiparametric MRI is highly sensitive, its use in multi-center AI workflows is hampered by inter-scanner variability. This study explores Federated Learning with radiomic features from DCE-MRI, and assesses the role of image standardization in improving TNBC classification performance. Data were split across 5 virtual clients to simulate hospitals, each training locally within a federated MLP framework. Results show that image standardization markedly improves TNBC classification, highlighting the role of preprocessing in federated AI pipelines.