用自监督学习提升肺结节检测准确率，无需标注数据
Lung Nodule-SSM: Self-Supervised Lung Nodule Detection and Classification in Thoracic CT Images
- 基于DINOv2在无标签CT图像上预训练，提取鲁棒特征
- 在LUNA16数据集上达到98.37%准确率，优于现有方法
- 适合医疗影像研究者和深度学习开发者参考
肺癌仍是近年来致死率最高的癌症之一，早期肺结节检测对改善患者预后至关重要。然而，医学影像标注数据有限，成为开发精准计算机辅助诊断（CAD）系统的主要瓶颈。自监督学习可利用大量未标注数据提升CAD系统的鲁棒性。随着基于Transformer的架构兴起及其在未见任务上的泛化能力，医疗领域正尝试将其应用于各类下游任务。本文提出一种新型方法LungNodule-SSM，采用DINOv2作为主干网络进行自监督学习，以增强肺结节检测与分类能力，且无需依赖标注数据。该方法分为两个阶段：首先在无标签CT扫描上预训练DINOv2模型，学习鲁棒特征表示；随后使用基于Transformer的架构对这些特征进行微调，实现病灶级检测与精确诊断。所提方法在具有挑战性的LUNA16数据集（包含888个CT扫描）上进行了评估，并与最先进方法对比。实验结果表明，本方法在肺结节检测中表现出色，准确率达98.37%，验证了其有效性。源代码、数据集及预处理数据可通过链接获取：https://github.com/EMeRALDsNRPU/Lung-Nodule-SSM-Self-Supervised-Lung-Nodule-Detection-and-Classification/tree/main
原文摘要 · Abstract (English)
Lung cancer remains among the deadliest types of cancer in recent decades, and early lung nodule detection is crucial for improving patient outcomes. The limited availability of annotated medical imaging data remains a bottleneck in developing accurate computer-aided diagnosis (CAD) systems. Self-supervised learning can help leverage large amounts of unlabeled data to develop more robust CAD systems. With the recent advent of transformer-based architecture and their ability to generalize to unseen tasks, there has been an effort within the healthcare community to adapt them to various medical downstream tasks. Thus, we propose a novel "LungNodule-SSM" method, which utilizes selfsupervised learning with DINOv2 as a backbone to enhance lung nodule detection and classification without annotated data. Our methodology has two stages: firstly, the DINOv2 model is pre-trained on unlabeled CT scans to learn robust feature representations, then secondly, these features are fine-tuned using transformer-based architectures for lesionlevel detection and accurate lung nodule diagnosis. The proposed method has been evaluated on the challenging LUNA 16 dataset, consisting of 888 CT scans, and compared with SOTA methods. Our experimental results show the superiority of our proposed method with an accuracy of 98.37%, explaining its effectiveness in lung nodule detection. The source code, datasets, and pre-processed data can be accessed using the link:https://github.com/EMeRALDsNRPU/Lung-Nodule-SSM-Self-Supervised-Lung-Nodule-Detection-and-Classification/tree/main
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