Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty
Deep networks segment brain tumours accurately in-distribution, but can fail silently when the input differs from their training data. That risk is central to clinical deployment and is the premise of the BraTS-GoAT generalizability task. We ask not only how well a model segments, but whether its uncertainty knows when it is wrong. On BraTS-GoAT (Task 3) we train a 5-fold cross-validated nnU-Net baseline (one held-out prediction per case) and a 3-seed deep ensemble. Both are evaluated for calibration and error detection on a per-region relevant mask, aggregated per case. In-distribution the 3-seed ensemble improves modestly over the already strong single model on the same held-out split, ...
| Authors | Riya Deepak Shet, Chenxi Liang, Le Zhang |
|---|---|
| Primary category | cs.CV |
| Published | 13 August 2026 |
| Last updated at source | 26 August 2026 |
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