PanCancerSeg Specialized Weights
Cancer-specific nnUNet v2 segmentation models trained on CT images from the CVPR 2026 FLARE Task 1: Pan-cancer Segmentation dataset.
Models
| Folder | Cancer type |
|---|---|
| Dataset102_Kidney | Kidney cancer |
| Dataset103_Liver | Liver cancer |
| Dataset104_Pancreas | Pancreatic cancer |
| Dataset105_Lung | Lung cancer |
All models use nnUNetTrainerWandb2000, nnUNetResEncUNetMPlans, 3d_fullres, fold 0, trained for 2000 epochs. Each folder contains checkpoint_best.pth (best validation checkpoint).
Usage
Download the weights and point --model_dir at the root directory:
# Clone this repo
git lfs install
git clone https://huggingface.co/KS987/PanCancerSeg-Specialized-weights
# Run inference
python predict.py \
--input /path/to/case.nii.gz \
--cancer_type kidney_cancer \
--model_dir ./PanCancerSeg-Specialized-weights \
--device cuda
See PanCancerSeg-Inference for the full inference pipeline.
File Structure
Dataset10X_*/
└── nnUNetTrainerWandb2000__nnUNetResEncUNetMPlans__3d_fullres/
├── dataset.json
├── dataset_fingerprint.json
├── plans.json
└── fold_0/
└── checkpoint_best.pth
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