Radiomics NnUNet

--- license: other library_name: nnunet tags: - medical-imaging - ct - radiomics - nnunet - pancreatic-cancer - pdac - panorama pipeline_tag: image-segmentation --- # Radiomics nnU-Net for PDAC Detection This model repository contains the nnU-Net checkpoints for the ISBI 2026 work: **From Global Radiomics to Parametric Maps: A Unified Workflow Fusing Radiomics and Deep Learning for PDAC Detection** Paper page: <https://huggingface.co/papers/2602.17986> GitHub code and reproduction instructions: <https://github.com/briandzt/dl-pdac-radiomics-global-n-paramaps> ## Model Description The workflow combines: - Stage-1 low-resolution pancreas localization using nnU-Net. - Voxel radiomics parametric map extraction. - Global radiomics feature extraction. - Stage-2 full-resolution PDAC detection using an nnU-Net variant with radiomics channels and global radiomics features. The public checkpoint layout mirrors the GitHub repository's expected `src/nnUNet_results` structure so users can download the model files directly into a fresh clone. ## Repository Layout ```text Dataset001_LR/ └── nnUNetTrainer__nnUNetPlans__3d_fullres/ ├── dataset.json ├── dataset_fingerprint.json ├── plans.json ├── fold_0/checkpoint_best.pth ├── fold_1/checkpoint_best.pth ├── fold_2/checkpoint_best.pth ├── fold_3/checkpoint_best.pth └── fold_4/checkpoint_best.pth Dataset002_stage2/ └── nnUNetTrainer_Loss_CE_checkpoints__nnUNetPlans__3d_fullres/ ├── dataset.json ├── dataset_fingerprint.json ├── plans.json ├── fold_0/checkpoint_best.pth ├── fold_1/checkpoint_best.pth ├── fold_2/checkpoint_best.pth ├── fold_3/checkpoint_best.pth └── fold_4/checkpoint_best.pth ``` ## Intended Use This release is intended for research reproducibility and method comparison for PDAC detection on venous-phase pancreatic CT. The workflow outputs: - a voxel-level PDAC detection map - a case-level PDAC likelihood JSON file This model is not intended for clinical deployment or standalone diagnosis. ## How to Use Clone the GitHub code repository, then download the checkpoints: ```powershell powershell -ExecutionPolicy Bypass -File .\scripts\download_checkpoints_from_hf.ps1 ` -RepoId briandzt/radiomics_nnUNet ``` Verify the local setup without a GPU: ```bash python scripts/verify_repository.py ``` Run local inference: ```bash python main.py -i ./workspace/test_example/input...