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license: apache-2.0
tags:
- medical-imaging
- segmentation
- nnunet
- ct
datasets:
- FLARE-MedFM/FLARE-Task1-Pancancer
---
# PanCancerSeg Specialized Weights
Cancer-specific nnUNet v2 segmentation models trained on CT images from the [CVPR 2026 FLARE Task 1: Pan-cancer Segmentation](https://www.codabench.org/competitions/7149/) 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:
```bash
# 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](https://github.com/Kappapapa123/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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