Background. The differential diagnosis of subcentimetre lung nodules with a diameter of less than 1 cm has always been one of the problems of imaging doctors and thoracic surgeons. We plan to create a deep learning model for the diagnosis of pulmonary nodules in a simple method. Methods. Image data and pathological diagnosis of patients come from the First Affiliated Hospital of Zhejiang University School of Medicine from October 1, 2016, to October 1, 2019. After data preprocessing and data augmentation, the training set is used to train the model. The test set is used to evaluate the trained model. At the same time, the clinician will also diagnose the test set. Results. A total of 2,295 images of 496 lung nodules and their corresponding pathological diagnosis were selected as a training set and test set. After data augmentation, the number of training set images reached 12,510 images, including 6,648 malignant nodular images and 5,862 benign nodular images. The area under the - curve of the trained model is 0.836 in the classification of malignant and benign nodules. The area under the ROC curve of the trained model is 0.896 (95% CI: 78.96%~100.18%), which is higher than that of three doctors. However, the value is not less than 0.05. Conclusion. With the help of an automatic machine learning system, clinicians can create a deep learning pulmonary nodule pathology classification model without the help of deep learning experts. The diagnostic efficiency of this model is not inferior to that of the clinician.

中文翻译：

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一种训练肺结节AI诊断模型的简单方法。

背景。直径小于1厘米的亚厘米肺结节的鉴别诊断一直是影像医生和胸外科医生的难题之一。我们计划以一种简单的方法创建一个用于诊断肺结节的深度学习模型。方法。2016年10月1日至2019年10月1日，患者的影像数据和病理诊断来自浙江大学医学院附属第一医院。经过数据预处理和数据增强后，使用训练集训练模型。测试集用于评估训练好的模型。同时，临床医生也会对测试集进行诊断。结果. 选取496个肺结节的2295张图像及其相应的病理诊断作为训练集和测试集。数据增强后，训练集图像数量达到12510张，其中恶性结节图像6648张，良性结节图像5862张。在良恶性结节分类中，训练模型的-曲线下面积为0.836。训练模型的ROC曲线下面积为0.896（95% CI：78.96%~100.18%），高于三位医生。但是，该值不小于0.05。结论. 借助自动机器学习系统，临床医生可以在没有深度学习专家帮助的情况下创建深度学习肺结节病理分类模型。该模型的诊断效率不亚于临床医生。