利用压缩与熵最大化实现图像引导手术辅助的手术基础模型
Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance
AI总结：
本文提出自监督框架 Compress-to-Explore（C2E），利用柯尔莫哥洛夫复杂度和熵最大化解码器从无标注手术视频中学习紧凑表示，构建手术视觉基础模型，在多种手术任务上实现更强的泛化性能。
摘要：
实时视频理解对于指导微创手术（MIS）中的操作流程至关重要。然而，监督学习方法需要大规模标注数据集，而由于医学等领域标注工作量大、成本高昂，此类数据集十分稀缺。尽管自监督方法可以缓解这些限制，但现有的自监督方法往往无法以跨任务可泛化的形式捕捉结构和物理信息。我们提出 Compress-to-Explore（C2E），一种新颖的自监督框架，利用柯尔莫哥洛夫复杂度从手术视频中学习紧凑且信息丰富的表示。C2E 使用熵最大化解码器对图像进行压缩，同时保留临床相关细节，从而在无需标注数据的情况下提升编码器性能。在大规模无标注手术数据集上训练后，C2E 在多种手术机器学习任务上展现出强大的泛化能力，包括工作流分类、工具-组织交互分类、分割和诊断任务，作为手术视觉基础模型提供了更优的性能。正如我们在论文中进一步展示的，该模型内部的紧凑表示能够更好地解耦图像中不同结构部分的特征。由此带来的性能提升凸显了自监督学习在增强手术人工智能和改善微创手术预后方面尚未开发的潜力。
英文摘要：
Real-time video understanding is critical to guide procedures in minimally invasive surgery (MIS). However, supervised learning approaches require large, annotated datasets that are scarce due to annotation efforts that are prohibitive, e.g., in medical fields. Although self-supervision methods can address such limitations, current self-supervised methods often fail to capture structural and physical information in a form that generalizes across tasks. We propose Compress-to-Explore (C2E), a novel self-supervised framework that leverages Kolmogorov complexity to learn compact, informative representations from surgical videos. C2E uses entropy-maximizing decoders to compress images while preserving clinically relevant details, improving encoder performance without labeled data. Trained on large-scale unlabeled surgical datasets, C2E demonstrates strong generalization across a variety of surgical ML tasks, such as workflow classification, tool-tissue interaction classification, segmentation, and diagnosis tasks, providing improved performance as a surgical visual foundation model. As we further show in the paper, the model's internal compact representation better disentangles features from different structural parts of images. The resulting performance improvements highlight the yet untapped potential of self-supervised learning to enhance surgical AI and improve outcomes in MIS.
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。
