README.txt

KM-UNet: KAN Mamba UNet for medical image segmentation paper:https://arxiv.org/abs/2501.02559 Medical image segmentation is a critical task in medical imaging analysis. Traditional CNN-based methods struggle with modeling long-range dependencies, while Transformer-based models, despite their success, suffer from quadratic computational complexity. To address these limitations, we propose KM-UNet, a novel U-shaped network architecture that combines the strengths of Kolmogorov-Arnold Networks (KANs) and state-space models (SSMs). KM-UNet leverages the Kolmogorov-Arnold representation theorem for efficient feature representation and SSMs for scalable long-range modeling, achieving a balance between accuracy and computational efficiency. We evaluate KM-UNet on five benchmark datasets: ISIC17, ISIC18, CVC, BUSI, and GLAS. Experimental results demonstrate that KM-UNet achieves competitive performance compared to state-of-the-art methods in medical image segmentation tasks. To the best of our knowledge, KM-UNet is the first medical image segmentation framework integrating KANs and SSMs. This work provides a valuable baseline and new insights for the development of more efficient and interpretable medical image segmentation systems. Keywords:KAN,Manba, state-space models,UNet, Medical image segmentation, Deep learning

train:python train.py --arch KM_UNet --dataset {dataset} --input_w {input_size} --input_h {input_size} --name {dataset}_KM-UNet --data_dir [YOUR_DATA_DIR]

For example:python train.py --arch KM_UNet --dataset busi --input_w 256 --input_h 256 --name busi_KM-UNet --data_dir ./inputs

The input folder contains the five datasets used in the experiment,it is opening datasets,can download in Kaggle. The data format is: Dataset name: images -1.png -2.png ... masks -1.png -2.png ... and the outputs folder contains the training data obtained from the article training(the weight file was larger than 25M,if you need,send me email)