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FLARE-AutoMSC Validation Baselines — Glioma IDH / Grade
Baseline models for MICCAI FLARE 2026 Task 2 — AutoMSC: Automated Medical Image Segmentation and Classification, on the GliomaIDHType cohort (491 multi-sequence brain MRI cases).
Each model jointly performs tumor segmentation and a case-level classification using the
multi-task nnUNet framework (nnUNetCLSTrainerMTL). Both models are trained with 5-fold
cross-validation and share the same images, labels, and fold splits — they differ only in the
classification head. This repository contains the best-checkpoint weights (checkpoint_best.pth)
for each fold plus the cross-validation evaluation report and figures.
Models
| Model | Modality | Cases | Segmentation | Classification (cases per class) | Mean DSC | Mean AUROC |
|---|---|---|---|---|---|---|
| Dataset007_GliomaIDHType | FLAIR, T1, T1ce, T2 | 491 | Tumor (1) | Grade (3-class): Grade 2 (80), Grade 3 (78), Grade 4 (333) | 0.875 | 0.825 |
| Dataset072_GliomaIDHType | FLAIR, T1, T1ce, T2 | 491 | Tumor (1) | IDH (binary): wild-type (350), mutated (141) | 0.873 | 0.912 |
Metrics are means across the 5 CV folds (each case scored by its held-out fold). AUROC is macro for
the 3-class grade model and binary for the IDH model. See each model's README.md for per-fold
tables and figures.
Repository Structure
DatasetXXX_GliomaIDHType/
├── README.md # per-fold results and figures (same as results.md)
├── results.md
├── figures/ # box plots, ROC/PRC, confusion matrix, case samples
└── nnUNetCLSTrainerMTL__nnUNetPlans__3d_fullres/
├── dataset.json
├── dataset_fingerprint.json
├── plans.json
├── eval_results/ # per-case CSVs, fold_summary.csv, summary.json
├── fold_0/
│ └── checkpoint_best.pth
├── ...
└── fold_4/
└── checkpoint_best.pth
combine_predictions.py (repo root) merges the IDH (binary) and grade (3-class) prediction CSVs
into the FLARE-AutoMSC submission format
(identifier, idh_wildtype, idh_mutated, grade2, grade3, grade4).
Reproduction
Cross-validation evaluation and figures are produced by fivefold_eval.py and
eval-report/generate_figures.py from the AutoMSC-Baseline codebase
(github.com/medfm-flare/AutoMSC-Baseline); the
raw dataset provides imagesTr/, labelsTr/, splits_final.json, cls_data.csv, and
cls_data_idh.csv.