发表机构
Yale University; University of Florida; Wichita State University; Purdue University; University of California, Berkeley; Institute of Computing Technology, Chinese Academy of Sciences; Stevens Institute of Technology; Zhejiang University（耶鲁大学; 佛罗里达大学; 威奇托州立大学; 普渡大学; 加利福尼亚大学伯克利分校; 中国科学院计算技术研究所; 史蒂文斯理工学院; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对医学图像分割模型在资源受限平台部署的挑战，提出分层聚类压缩方法MIS-HCC，利用瓦瑟斯坦距离表示通道相似性形成矩阵指导聚类，融合通道生成压缩网络，实验证明其在精度和压缩效率上优于现有方法。
AI 中文摘要
医学图像分割模型需要高精度和轻量级设计以适应实际医学应用。由于其高计算和参数要求，在资源有限的医学平台上部署这些模型仍然是一项重大挑战。现有的模型压缩剪枝方法大多忽略了复杂深度神经网络内部结构之间的内在联系和相似性。因此，压缩模型可能无法有效保留预训练网络的基本特征。为解决此问题，我们提出了一种用于医学图像分割模型的分层聚类压缩方法（MIS-HCC）。该方法采用分层聚类对通道进行划分并有效融合其参数。具体而言，它利用瓦瑟斯坦距离来表示预训练网络各层内通道的相似性，形成一个指导聚类过程的相似性矩阵。然后将每个聚类中的通道融合以生成压缩网络。在三个医学图像数据集上的实验结果表明，MIS-HCC在准确性和压缩效率方面均优于现有方法，为在资源有限的医学平台上部署医学图像分割模型提供了有效解决方案。
英文摘要
Medical image segmentation models require both high accuracy and lightweight design to accommodate real-world medical applications. The deployment of these models on resource-limited medical platforms remains a significant challenge due to their high computational and parameter requirements. Existing pruning methods for model compression mostly overlook the intrinsic connections and similarity between the internal structures of complex deep neural networks. As a result, compressed models may not effectively retain the basic features of the pretrained network. To solve this problem, we propose a hierarchical clustering compression method for medical image segmentation models (MIS-HCC). This approach employs hierarchical clustering to partition channels and fuse their parameters efficiently. Specifically, it leverages the Wasserstein distance to represent similarity of channels within layers of pre-trained network, forming a similarity matrix that guides the clustering process. Channels within each cluster are then fused to produce a compressed network. Experimental results on three medical image datasets application demonstrate that MIS-HCC outperforms the state-of-the-art methods in both accuracy and compression efficiency, offering an effective solution for deploying medical image segmentation models on resource-limited medical platforms.
