Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential region-of-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4%, 92%, 96% and 98.51% with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People’s Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7% sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients.

中文翻译：

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基于云的胸部 CT 肺癌检测和诊断自动化临床决策支持系统


肺癌是癌症相关死亡的主要原因。早期发现肺癌可以大大提高生存率。放射科医生手动描绘肺结节是一项繁琐的任务。我们开发了一种基于 3D 深度卷积神经网络 (3DDCNN) 的新型肺结节检测计算机辅助决策支持系统，以协助放射科医生。我们的决策支持系统为放射科医生在肺癌诊断决策中提供了第二意见。为了利用计算机断层扫描 (CT) 扫描的 3 维信息，我们应用中值强度投影和多区域提议网络 (mRPN) 来自动选择潜在的感兴趣区域。我们的计算机辅助诊断 (CAD) 系统已使用 LUNA16、ANODE09 和 LIDC-IDR 数据集进行了训练和验证；实验证明了我们的系统的卓越性能，每次扫描 2.1 FP 时，灵敏度、特异性、AUROC 和准确度分别达到 98.4%、92%、96% 和 98.51%。我们集成了云计算，在上海第六人民医院提供的数据集以及 LUNA16、ANODE09 和 LIDC-IDR 上训练和验证了基于云的 3DDCNN。我们的系统超越了最先进的系统，每次扫描 1.97 FP 时的灵敏度高达 98.7%，令人印象深刻。这显示了深度学习与云计算相结合，通过 CT 成像准确有效地检测肺结节的潜力，这可以帮助医生和放射科医生治疗肺癌患者。