Yang0822/SlimFormerPlusPlus-AMOS2022-CT

SlimFormer++ — AMOS2022 CT

Official fold-0 pretrained checkpoint for 3D CT only medical image segmentation, released with SlimFormer++: Token Mixing Where It Matters for Efficient 3D Medical Image Segmentation.

Source code · Paper · Hugging Face Papers · Original checkpoint release · Dataset

This repository mirrors the original inference-only checkpoint without changing the selected network weights, along with its nnU-Net plans and dataset metadata. Training images, segmentation masks, and optimizer state are not distributed here.

Model

SlimFormer++ is a custom nnU-Net v2 trainer. It uses windowed Mamba mixing in the first encoder stage, residual channel MLPs in deeper stages, and a depthwise-convolution bottleneck augmented with sparse long-range context. This checkpoint is for the original Mamba-based model. The separate Mamba-free implementation has no released pretrained checkpoint.

Setting	Value
Dataset	AMOS2022 CT (Dataset004_AMOS)
Input	One CT only channel, NIfTI .nii.gz
Trainer	nnUNetTrainer_SlimFormerPlusPlus
Configuration / fold	3d_fullres / 0
Selected checkpoint epoch	1199
Completed training epochs	1200
Training / validation cases	200 / 100
Inference patch	[64, 192, 160]

Output labels

Label ID	Structure
0	background
1	spleen
2	right kidney
3	left kidney
4	gallbladder
5	esophagus
6	liver
7	stomach
8	aorta
9	inferior vena cava
10	pancreas
11	right adrenal gland
12	left adrenal gland
13	duodenum
14	bladder
15	prostate/uterus

Evaluation

The paper's Table III results and the released checkpoint's fold-0 validation measurements are reported separately. They are different evaluation records. The fold-0 results are not independent test-set results.

Evaluation record	Mean Dice (%)	Mean HD95 (mm)
Paper, Table III	89.76	11.36
Released checkpoint, fold-0 validation (100 cases)	90.4005	10.0117

The validation aggregates are copied from the project's published reproduction materials. The protocol records training and inference settings; metrics contains the reference aggregates. See the reproduction guide for data preparation, original splits, per-case results and evaluation commands.

Use the checkpoint

1. Follow the project's installation guide. The reference setup uses Linux, an NVIDIA GPU, Python 3.12, PyTorch 2.6.0 and CUDA Toolkit 12.6. Mamba and causal-conv1d are compiled from source. Source revision used for this model card: 92e66fcd8428bd9aeae94e44df36842078c60006.
2. Install huggingface_hub in that environment and download this repository into a fresh nnU-Net results directory:

python -m pip install huggingface_hub
export nnUNet_results=/path/to/empty/nnUNet_results
hf download Yang0822/SlimFormerPlusPlus-AMOS2022-CT --local-dir "$nnUNet_results"
(cd "$nnUNet_results" && sha256sum -c SHA256SUMS)

3. Prepare one input volume per case as CASE_ID_0000.nii.gz, using the matching CT only modality, and run:

export nnUNet_compile=false
nnUNetv2_predict -i /path/to/imagesTs -o /path/to/predictions \
    -d 4 -c 3d_fullres \
    -tr nnUNetTrainer_SlimFormerPlusPlus -f 0 \
    -chk checkpoint_best.pth -step_size 0.5

The original dataset/model directory structure is preserved so the nnU-Net predictor can discover the checkpoint. Downloading files does not install the trainer or its CUDA dependencies. This model uses the project's custom nnU-Net runtime; see the commands above for inference.

Intended use and limitations

The release is intended for noncommercial scientific research and reproducibility. Its validation results describe the published dataset split and do not establish performance on other scanners, institutions, populations or imaging protocols. This release does not establish suitability for clinical diagnosis or deployment. Dataset access and use remain subject to the dataset providers' terms.

License and provenance

Noncommercial research use only. The existing SlimFormer++ Noncommercial Research License 1.0 is preserved. Third-party components retain their own licenses and notices; see NOTICE.md and Apache-2.0.

provenance.json records the original release URL, archive SHA256, checkpoint SHA256 and source revision. SHA256SUMS is the unmodified checksum list from the original GitHub checkpoint archive.

Citation

Please cite SlimFormer++ together with nnU-Net:

@misc{hong2026slimformertokenmixingmatters,
      title={SlimFormer++: Token Mixing Where It Matters for Efficient 3D Medical Image Segmentation},
      author={Yang Hong and Lei Zhang and Zhihong Liu and Jianqing Mo and Xujiong Ye},
      year={2026},
      eprint={2610.25488},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2610.25488},
}

nnU-Net source and citation