Path-SAM2：将SAM2迁移用于数字病理语义分割
Path-SAM2: Transfer SAM2 for digital pathology semantic segmentation
AI总结：
本文提出Path-SAM2，首次将SAM2适配到病理语义分割任务，通过融合UNI病理视觉编码器并引入可学习KAN分类模块替代手动提示，在三个腺瘤病理数据集上取得了最先进性能。
摘要：
病理学中的语义分割任务在辅助医生判断组织病变状况方面发挥着不可或缺的作用。随着Segment Anything Model（SAM）的提出，越来越多的基础模型在图像分割领域得到了快速发展。近期，SAM2在自然图像和医学图像分割中引起了广泛关注。与SAM相比，它在分割精度和泛化性能方面都有显著提升。我们对基于SAM的基础模型进行了比较，发现它们在病理图像语义分割中的表现难以令人满意。在本文中，我们提出了Path-SAM2，首次对SAM2模型进行适配，以服务于病理语义分割任务。我们将最大的组织病理学预训练视觉编码器（UNI）与原始SAM2编码器进行整合，加入了更多基于病理学的先验知识。此外，我们引入了一个可学习的Kolmogorov-Arnold Networks（KAN）分类模块来替代手动提示过程。在三个腺瘤病理数据集上，Path-SAM2取得了最先进的性能。本研究展示了将SAM2适配到病理图像分割任务中的巨大潜力。我们计划在以下地址发布本文的代码和模型权重：https://github.com/simzhangbest/SAM2PATH
英文摘要：
The semantic segmentation task in pathology plays an indispensable role in assisting physicians in determining the condition of tissue lesions. With the proposal of Segment Anything Model (SAM), more and more foundation models have seen rapid development in the field of image segmentation. Recently, SAM2 has garnered widespread attention in both natural image and medical image segmentation. Compared to SAM, it has significantly improved in terms of segmentation accuracy and generalization performance. We compared the foundational models based on SAM and found that their performance in semantic segmentation of pathological images was hardly satisfactory. In this paper, we propose Path-SAM2, which for the first time adapts the SAM2 model to cater to the task of pathological semantic segmentation. We integrate the largest pretrained vision encoder for histopathology (UNI) with the original SAM2 encoder, adding more pathology-based prior knowledge. Additionally, we introduce a learnable Kolmogorov-Arnold Networks (KAN) classification module to replace the manual prompt process. In three adenoma pathological datasets, Path-SAM2 has achieved state-of-the-art performance.This study demonstrates the great potential of adapting SAM2 to pathology image segmentation tasks. We plan to release the code and model weights for this paper at: https://github.com/simzhangbest/SAM2PATH
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
