U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

📌 This is an official PyTorch implementation of U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation

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U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
Chenxin Li*, Xinyu Liu*, Wuyang Li*, Cheng Wang*, Hengyu Liu, Yixuan Yuan^✉
The Chinese Univerisity of Hong Kong

We explore the untapped potential of Kolmogorov-Anold Network (aka. KAN) in improving backbones for vision tasks. We investigate, modify and re-design the established U-Net pipeline by integrating the dedicated KAN layers on the tokenized intermediate representation, termed U-KAN. Rigorous medical image segmentation benchmarks verify the superiority of U-KAN by higher accuracy even with less computation cost. We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures. These endeavours unveil valuable insights and sheds light on the prospect that with U-KAN, you can make strong backbone for medical image segmentation and generation.

📰News

[2024.6] Code and paper of U-KAN are released!

💡Key Features

The first effort to incorporate the advantage of emerging KAN to improve established U-Net pipeline to be more accurate, efficient and interpretable.
A Segmentation U-KAN with tokenized KAN block to effectively steer the KAN operators to be compatible with the exiting convolution-based designs.
A Diffusion U-KAN as an improved noise predictor demonstrates its potential in backboning generative tasks and broader vision settings.

🛠Setup

git clone https://github.com/CUHK-AIM-Group/U-KAN.git
cd U-KAN
conda create -n ukan python=3.10
conda activate ukan
cd Seg_UKAN && pip install -r requirements.txt

Tips A: We test the framework using pytorch=1.13.0, and the CUDA compile version=11.6. Other versions should be also fine but not totally ensured.

📚Data Preparation

BUSI: The dataset can be found here.

GLAS: The dataset can be found here.

CVC-ClinicDB: The dataset can be found here.

We also provide all the pre-processed dataset without requiring any further data processing. You can directly download and put them into the data dir.

The resulted file structure is as follows.

Seg_UKAN
├── inputs
│   ├── busi
│     ├── images
│           ├── malignant (1).png
|           ├── ...
|     ├── masks
│        ├── 0
│           ├── malignant (1)_mask.png
|           ├── ...
│   ├── GLAS
│     ├── images
│           ├── 0.png
|           ├── ...
|     ├── masks
│        ├── 0
│           ├── 0.png
|           ├── ...
│   ├── CVC-ClinicDB
│     ├── images
│           ├── 0.png
|           ├── ...
|     ├── masks
│        ├── 0
│           ├── 0.png
|           ├── ...

🔖Evaluating Segmentation U-KAN

You can directly evaluate U-KAN from the checkpoint model. Here is an example for quick usage for using our pre-trained models in Segmentation Model Zoo:

Download the pre-trained weights and put them to {args.output_dir}/{args.name}/model.pth
Run the following scripts to

cd Seg_UKAN
python val.py --name ${dataset}_UKAN --output_dir [YOUR_OUTPUT_DIR]

⏳Training Segmentation U-KAN

You can simply train U-KAN on a single GPU by specifing the dataset name --dataset and input size --input_size.

cd Seg_UKAN
python train.py --arch UKAN --dataset ${dataset} --input_w ${input_size} --input_h ${input_size} --name ${dataset}_UKAN  --data_dir [YOUR_DATA_DIR]

For example, train U-KAN with the resolution of 256x256 with a single GPU on the BUSI dataset in the inputs dir:

cd Seg_UKAN
python train.py --arch UKAN --dataset busi --input_w 256 --input_h 256 --name busi_UKAN  --data_dir ./inputs

Please see Seg_UKAN/scripts.sh for more details.

🎪Segmentation Model Zoo

Here is an overview of performance&checkpoints.

Method	Dataset	IoU	F1	Checkpoints
Seg U-KAN	BUSI	63.38	76.40	Link
Seg U-KAN	GLAS	87.64	93.37	Link
Seg U-KAN	CVC-ClinicDB	85.05	91.88	Link

🎇Medical Image Generation with Diffusion U-KAN

Please refer to Diffusion_UKAN

🛒TODO List

Release code for Seg U-KAN.
Release code for Diffusion U-KAN.
Upload the pretrained checkpoints.

🎈Acknowledgements

Greatly appreciate the tremendous effort for the following projects!

CKAN

📜Citation

If you find this work helpful for your project,please consider citing the following paper:

@article{li2024ukan,
  author    = {Chenxin Li and Xinyu Liu and Wuyang Li and Cheng Wang and Hengyu Liu and Yixuan Yuan},
  title     = {U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation},
  journal   = {arXiv preprint arXiv:2406.02918},
  year      = {2024}
}

CUHK-AIM-Group / U-KAN