文章：

用于计算机断层扫描图像中肺结节计算机辅助诊断的可解释三维人工智能模型

An Interpretable Three-Dimensional Artificial Intelligence Model for Computer-Aided Diagnosis of Lung Nodules in Computed Tomography Images

原文发布日期：21 September 2023

DOI: 10.3390/cancers15184655

类型: Article

开放获取: 是

英文摘要：

Lung cancer is typically classified into small-cell carcinoma and non-small-cell carcinoma. Non-small-cell carcinoma accounts for approximately 85% of all lung cancers. Low-dose chest computed tomography (CT) can quickly and non-invasively diagnose lung cancer. In the era of deep learning, an artificial intelligence (AI) computer-aided diagnosis system can be developed for the automatic recognition of CT images of patients, creating a new form of intelligent medical service. For many years, lung cancer has been the leading cause of cancer-related deaths in Taiwan, with smoking and air pollution increasing the likelihood of developing the disease. The incidence of lung adenocarcinoma in never-smoking women has also increased significantly in recent years, resulting in an important public health problem. Early detection of lung cancer and prompt treatment can help reduce the mortality rate of patients with lung cancer. In this study, an improved 3D interpretable hierarchical semantic convolutional neural network named HSNet was developed and validated for the automatic diagnosis of lung cancer based on a collection of lung nodule images. The interpretable AI model proposed in this study, with different training strategies and adjustment of model parameters, such as cyclic learning rate and random weight averaging, demonstrated better diagnostic performance than the previous literature, with results of a four-fold cross-validation procedure showing calcification: 0.9873 ± 0.006, margin: 0.9207 ± 0.009, subtlety: 0.9026 ± 0.014, texture: 0.9685 ± 0.006, sphericity: 0.8652 ± 0.021, and malignancy: 0.9685 ± 0.006.

摘要翻译： 

肺癌通常分为小细胞癌与非小细胞癌两大类，其中非小细胞癌约占所有肺癌病例的85%。低剂量胸部计算机断层扫描（CT）能够快速、无创地诊断肺癌。在深度学习时代，可开发人工智能（AI）计算机辅助诊断系统，实现对患者CT图像的自动识别，从而开创智能医疗服务的新模式。多年来，肺癌始终是台湾地区癌症相关死亡的首要原因，吸烟与空气污染均会增加患病风险。近年来非吸烟女性肺腺癌发病率亦显著上升，已成为重要的公共卫生问题。早期发现肺癌并及时治疗有助于降低肺癌患者的死亡率。本研究基于肺部结节图像数据集，开发并验证了一种改进的三维可解释分层语义卷积神经网络（HSNet），用于肺癌的自动诊断。本研究提出的可解释AI模型通过采用循环学习率、随机权重平均等不同训练策略及参数调整，其诊断性能优于既往文献报道，四折交叉验证结果显示：钙化度0.9873±0.006、边缘特征0.9207±0.009、细微度0.9026±0.014、纹理特征0.9685±0.006、球形度0.8652±0.021、恶性程度0.9685±0.006。

原文链接：

An Interpretable Three-Dimensional Artificial Intelligence Model for Computer-Aided Diagnosis of Lung Nodules in Computed Tomography Images