Deep learning models show reliability issues in brain tumor segmentation

A new study published on arXiv investigates the reliability of deep learning models for brain tumor segmentation, specifically focusing on the BraTS-GoAT dataset. Researchers evaluated a standard nnU-Net model and a deep ensemble, finding that while the ensemble offered modest improvements in calibration and accuracy on in-distribution data, its performance degraded significantly under synthetic corruptions simulating acquisition shifts. The study highlights that inter-member disagreement in ensembles is a more sensitive indicator of these shifts than single-model confidence, though per-voxel error localization weakens with increasing severity of corruption. AI

IMPACT Highlights the need for robust uncertainty quantification in medical AI to ensure safe clinical deployment.

RANK_REASON The cluster contains a research paper detailing a controlled robustness study of deep-ensemble uncertainty for medical image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• BraTS-GoAT
• nnU-Net

• paper
• safety

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