arXiv Computer Vision
Aug 27

Reliability analysis for BraTS-GoAT segmentation: a controlled robustness study of deep-ensemble uncertainty

The study evaluates the reliability of deep‑ensemble uncertainty for brain tumour segmentation on the BraTS‑GoAT dataset. A 5‑fold cross‑validated nnU‑Net baseline and a 3‑seed deep ensemble were compared for calibration and error detection; the ensemble showed modest gains in calibration on in‑distribution data but the single model’s confidence remained flat while accuracy degraded under synthetic corruptions. Disagreement among ensemble members rose sharply with corruption severity, proving to be a more sensitive indicator of acquisition shift than single‑model confidence.

By Riya Deepak Shet, Chenxi Liang, Le Zhang
arXiv AI
Oct 9

Missing Modality-Aware Calibration for Trustworthy Brain Tumor Segmentation

The paper introduces Missing Modality-Aware Local Temperature Scaling (MMA‑LTS), a post‑hoc voxel‑wise confidence calibration technique designed for multimodal brain tumor segmentation. MMA‑LTS adapts a temperature field based on which MRI modalities are present and a voxel‑wise difficulty score, addressing calibration errors that arise when specific modalities are missing. Experiments on BraTS 2020 and FeTS 2024 demonstrate that MMA‑LTS improves calibration while maintaining segmentation accuracy across various missing‑modality scenarios.

By Sol Lee, Hyunji Kim, Sungrae Hong, Donghee Han, Mun Yi