Vanilla nnUNet v2 Baseline — CVPR 2026 Task 1: Pan-Cancer Segmentation

Binary lesion segmentation from CT using vanilla nnU-Net v2 (ResEncM). Reference baseline for CVPR 2026: Foundation Models for Pan-cancer Segmentation in CT Images Task 1.

Code + failure analysis: GitHub — Kappapapa123/CVPR2026-PanCancerSeg-baseline

Model Details

Parameter	Value
Framework	nnU-Net v2.6.4
Architecture	Residual Encoder U-Net Medium (`nnUNetResEncUNetMPlans`)
Configuration	3d_fullres
Task	Binary segmentation (0=background, 1=tumor)
Epochs	2000
Fold	0 (official competition split via `splits_final.json`)
Trainer class	`nnUNetTrainerWandb2000`
Dataset ID	101 (`Dataset101_FLAREPanCancer`)

Training Data

FLARE-MedFM/FLARE-Task1-Pancancer — 10,762 cases total:

train_label (~10,000 cases): Partially labeled (primary lesion only per case)
DeepLesion5K-MedSAM2 (~762 cases): Pseudo labels from MedSAM2

Only the primary lesion is annotated per training case. Metastatic lesions may be present but unlabeled.

Performance

Evaluated on 50 validation-public cases:

Metric	Mean	Median	Std
Lesion DSC	0.3278	0.2318	0.2883
Lesion NSD (2 mm)	0.1797	0.1046	0.2029

How to Use

# 1. Install dependencies
pip install nnunetv2==2.6.4 huggingface-hub

# 2. Clone code repo (needed for the custom trainer)
git clone https://github.com/Kappapapa123/CVPR2026-PanCancerSeg-baseline.git

# 3. Symlink the custom trainer into nnUNet
VARIANTS_DIR=$(python -c "import nnunetv2; print(nnunetv2.__path__[0])")/training/nnUNetTrainer/variants
ln -sf $(realpath CVPR2026-PanCancerSeg-baseline/trainers/nnUNetTrainerWandb2000.py) \
    "$VARIANTS_DIR/nnUNetTrainerWandb2000.py"

# 4. Download model weights
huggingface-cli download KS987/CVPR2026-PanCancerSeg-baseline \
    --local-dir ./model --repo-type model

# 5. Run inference
nnUNetv2_predict \
    -i /path/to/input_images \
    -o /path/to/output_predictions \
    -d 101 -c 3d_fullres -f 0 \
    -tr nnUNetTrainerWandb2000 \
    -p nnUNetResEncUNetMPlans \
    -m ./model/nnUNet/Dataset101_FLAREPanCancer \
    --disable_tta

Both checkpoint_final.pth and checkpoint_best.pth are provided. nnUNet uses checkpoint_final.pth by default.

Inference resampling requires ~64 GB RAM.

Limitations

Partial labels: Training data only labels the primary lesion per case.
False positive fragmentation: The dominant failure mode is scattered false-positive blobs. See the failure analysis.

Files

nnUNet/
  Dataset101_FLAREPanCancer/
    nnUNetTrainerWandb2000__nnUNetResEncUNetMPlans__3d_fullres/
      fold_0/
        checkpoint_best.pth      # Best validation checkpoint
        checkpoint_final.pth     # Final epoch (2000) checkpoint
      dataset.json
      dataset_fingerprint.json
      plans.json

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