Lung cancer is the leading cause of cancer deaths worldwide. Accurately diagnosing the malignancy of suspected lung nodules is of paramount clinical importance. However, to date, the pathologically-proven lung nodule dataset is largely limited and is highly imbalanced in benign and malignant distributions. In this study, we proposed a Semi-supervised Deep Transfer Learning (SDTL) framework for benign-malignant pulmonary nodule diagnosis. First, we utilize a transfer learning strategy by adopting a pre-trained classification network that is used to differentiate pulmonary nodules from nodule-like tissues. Second, since the size of samples with pathological-proven is small, an iterated feature-matching-based semi-supervised method is proposed to take advantage of a large available dataset with no pathological results. Specifically, a similarity metric function is adopted in the network semantic representation space for gradually including a small subset of samples with no pathological results to iteratively optimize the classification network. In this study, a total of 3,038 pulmonary nodules (from 2,853 subjects) with pathologically-proven benign or malignant labels and 14,735 unlabeled nodules (from 4,391 subjects) were retrospectively collected. Experimental results demonstrate that our proposed SDTL framework achieves superior diagnosis performance, with accuracy = 88.3%, AUC = 91.0% in the main dataset, and accuracy = 74.5%, AUC = 79.5% in the independent testing dataset. Furthermore, ablation study shows that the use of transfer learning provides 2% accuracy improvement, and the use of semi-supervised learning further contributes 2.9% accuracy improvement. Results implicate that our proposed classification network could provide an effective diagnostic tool for suspected lung nodules, and might have a promising application in clinical practice.

中文翻译：

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用于胸部 CT 图像中肺结节良恶性诊断的半监督深度迁移学习


肺癌是全世界癌症死亡的主要原因。准确诊断疑似肺结节的恶性程度具有重要的临床意义。然而，迄今为止，经过病理证实的肺结节数据集在很大程度上是有限的，并且良恶性分布高度不平衡。在这项研究中，我们提出了一种用于良恶性肺结节诊断的半监督深度迁移学习（SDTL）框架。首先，我们利用迁移学习策略，采用预先训练的分类网络，用于区分肺结节和结节样组织。其次，由于经过病理证实的样本量较小，因此提出了一种基于迭代特征匹配的半监督方法，以利用没有病理结果的大型可用数据集。具体来说，在网络语义表示空间中采用相似性度量函数，逐步包含没有病理结果的小样本子集，以迭代优化分类网络。在这项研究中，总共回顾性收集了3,038个经病理证实良性或恶性标记的肺结节（来自2,853名受试者）和14,735个未标记的结节（来自4,391名受试者）。实验结果表明，我们提出的 SDTL 框架实现了卓越的诊断性能，在主数据集中准确率 = 88.3%，AUC = 91.0%，在独立测试数据集中准确率 = 74.5%，AUC = 79.5%。此外，消融研究表明，使用迁移学习可以提高 2% 的准确率，而使用半监督学习可以进一步提高 2.9% 的准确率。 结果表明，我们提出的分类网络可以为疑似肺结节提供有效的诊断工具，并可能在临床实践中具有广阔的应用前景。