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README.md
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Baseline models for **MICCAI FLARE 2026 Task 2 — AutoMSC: Automated Medical Image Segmentation and Classification**.
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Each model jointly performs organ/lesion segmentation and case-level classification using a multi-task learning nnUNet framework (`nnUNetCLSTrainerMTL`). Models are trained with 5-fold cross-validation; this repository contains the best-checkpoint weights (`checkpoint_best.pth`) for each fold.
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## Datasets
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Baseline models for **MICCAI FLARE 2026 Task 2 — AutoMSC: Automated Medical Image Segmentation and Classification**.
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**Codebase:** [github.com/medfm-flare/AutoMSC-Baseline](https://github.com/medfm-flare/AutoMSC-Baseline)
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Each model jointly performs organ/lesion segmentation and case-level classification using a multi-task learning nnUNet framework (`nnUNetCLSTrainerMTL`). Models are trained with 5-fold cross-validation; this repository contains the best-checkpoint weights (`checkpoint_best.pth`) for each fold.
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## Datasets
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