AdaSwitch3D: Reliability-Aware Human-in-the-Loop Artificial Intelligence for Breast Tumor Segmentation in Dynamic Contrast-Enhanced MRI Across Unseen Centers

Background/Objectives: Deep learning models for breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) often lose accuracy at centers that did not contribute training data. We investigated whether case-wise routing between two low-resource adapted models, combined with an uncertainty-based review-prioritization signal, behaves consistently under severe cross-center domain shift.

Methods

Using the four public collections of the MAMA-MIA dataset (DUKE, ISPY1, ISPY2, NACT; 1231 cases), we performed four-fold leave-one-center-out experiments in which each held-out center contributed only five labeled cases and a pool of unlabeled scans. We compared source-only transfer, five-shot fine-tuning, and teacher-student self-training with AdaSwitch3D, which routes each case between the two adapted branches using 17 label-free features and produces a heuristic review-prioritization signal. The complete pipeline-data splits, five-shot selection, training, adaptation, router construction, and referral calibration-was repeated with five random seeds, and each seed was evaluated once on 80 untouched test cases (400 seed-specific test observations).