Assessing nnU-Net Generalization across Brain Tumor Populations in BraTS-GoAT 2026
A 3D nnU-Net trained on 1,351 BraTS-GoAT 2026 cases shows mean Dice dropping 0.0747 from out-of-fold to pooled validation, exposing generalization gaps.
Researchers trained a conventional 3D nnU-Net on 1,351 labeled brain tumor cases with five-fold cross-validation and 1,000 epochs per fold, averaging folds with test-time mirroring for the final predictor. On pooled official validation, global Dice scores were 0.7805 for enhancing tumor, 0.8288 for tumor core, and 0.8854 for whole tumor. Under matched fold-0 inference, mean regional Dice fell from 0.9058 on source out-of-fold cases to 0.8310 on pooled validation, and failures correlated with smaller and more disconnected enhancing tumor components rather than volume alone.
- 3D nnU-Net trained on 1,351 labeled BraTS-GoAT cases, five-fold CV
- Global DSC: 0.7805 ET, 0.8288 TC, 0.8854 WT on pooled validation
- Mean Dice dropped 0.0747 from OOF source cases to pooled validation
- Test-time mirroring gave small single-fold gains, no ensemble benefit
- Failures linked to smaller, more disconnected enhancing tumor components
- Tags
- brain-tumor-segmentationbrats-goatcross-validationdice-scoregeneralizationmedical-imagingnnu-net
- Products
- nnU-Net
- Organizations
- BraTS-GoAT
From the original article
1 min read at huggingface.co
BraTS-GoAT evaluates tumor segmentation across heterogeneous populations. We trained a conventional 3D nnU-Net on 1,351 labeled cases using five-fold cross-validation and 1,000 epochs per fold. The final predictor averaged all folds and applied test-time mirroring.
By Tristan Kirscher, Vivian Metzger, Philippe Meyer · huggingface.co
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