关键词: 小目标医学图像分割；低秩微调；KANConv；分割一切模型；高效微调 

Abstract: Medical image segmentation provides a basis for disease treatment and promotes the development of modern medicine by accurately identifying the key areas in medical images. Most of the traditional medical image segmentation methods are one model for one task, with high data dependence, large computing power requirements, and unstable cross-domain segmentation performance. Segmentation Anything Model (SAM) shows strong cross-modal adaptability and segmentation performance on natural images. However, there are significant differences between natural images and medical images, especially small object medical images, which makes SAM face problems of low efficiency and insufficient accuracy in the segmentation of small object medical images. To solve the above problems, an efficient fine-tuning small-object medical image segmentation method (LoRA-KANConv SAM) based on SAM was proposed. In view of the performance differences of pre-trained SAM on natural images and medical images, it was proposed to inject Low-Rank Adaptation of Large Language Models(LoRA) into the shallow network of the ViT（Vision Transformer） encoder of the SAM model to guide the initial feature extraction of the input image, reducing the number of parameters of the model while preserving the basic representation ability of the pre-trained model. The KANConv (Convolutional Kolmogorov-Arnold Networks) layer was applied to the model to extract the local detail features of the image and improve the accuracy of image segmentation. The experimental results show that the training parameters of LoRA-KANConv SAM are only 0.67% of the original parameters, and the four evaluation indicators Dice(Dice Similarity Coefficient)，IoU(Intersection over Union)，HD95(95th Percentile Hausdorff Distance)，ASSD (Average Symmetric Surface Distance) are greatly improved, the segmentation mask prediction speed reaches 0.33 s/f, and the switching time of the fine-tuning model is only 13.2% of the full fine-tuning model.

Key words: small object medical image segmentation, Low-Rank Adaptation of Large Language Models (LoRA), KANConv (Convolutional Kolmogorov-Arnold Networks), Segmentation Anything model (SAM), Parameter-Efficient Fine-Tuning