Upload README.md

README.md

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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
+```