一种用于COVID-19诊断的可解释非局部网络
An Explainable Non-local Network for COVID-19 Diagnosis
- Chengdu University of Information Technology（成都信息工程大学）
- Southwest Jiaotong University（西南交通大学）
- University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）
- Purdue University（普渡大学）
- University at Buffalo, SUNY（纽约州立大学布法罗分校）
- West China Hospital, Sichuan University（四川大学华西医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出深度残差3D注意力非局部网络NL-RAN，通过融合非局部全局建模与3D注意力病灶聚焦，在4079例CT扫描上实现COVID-19、普通肺炎与正常的快速可解释分类，AUC达0.9903，优于现有方法。
AI中文摘要：
卷积神经网络（CNN）在医学图像自动分类中已取得优异成果。在本研究中，我们提出了一种新颖的深度残差3D注意力非局部网络（NL-RAN），用于对包含COVID-19、普通肺炎和正常的CT图像进行分类，以实现快速且可解释的COVID-19诊断。我们构建了一个能够实现端到端训练的深度残差3D注意力非局部网络。该网络嵌入了非局部模块以捕获全局信息，同时嵌入了3D注意力模块以聚焦于病灶细节，从而可以直接分析3D肺部CT并输出分类结果。注意力模块的输出可用作热图，以提高模型的可解释性。本研究共纳入4079例3D CT扫描。每例扫描具有唯一标签（新型冠状病毒肺炎、普通肺炎和正常）。CT扫描队列被随机划分为3263例训练集、408例验证集和408例测试集。并与现有主流分类方法（如CovNet、CBAM、ResNet等）进行比较。同时将可视化结果与CAM等可视化方法进行比较。模型性能使用ROC曲线下面积（AUC）、精确率和F1-score进行评估。NL-RAN取得了0.9903的AUC、0.9473的精确率和0.9462的F1-score，超过了所有对比的分类方法。注意力模块输出的热图也比CAM输出的热图更清晰。我们的实验结果表明，我们提出的方法显著优于现有方法。此外，第一个注意力模块输出包含详细轮廓信息的热图，以提高模型的可解释性。我们的实验表明，该模型的推理速度很快，可以提供实时的诊断辅助。
英文摘要：
The CNN has achieved excellent results in the automatic classification of medical images. In this study, we propose a novel deep residual 3D attention non-local network (NL-RAN) to classify CT images included COVID-19, common pneumonia, and normal to perform rapid and explainable COVID-19 diagnosis. We built a deep residual 3D attention non-local network that could achieve end-to-end training. The network is embedded with a nonlocal module to capture global information, while a 3D attention module is embedded to focus on the details of the lesion so that it can directly analyze the 3D lung CT and output the classification results. The output of the attention module can be used as a heat map to increase the interpretability of the model. 4079 3D CT scans were included in this study. Each scan had a unique label (novel coronavirus pneumonia, common pneumonia, and normal). The CT scans cohort was randomly split into a training set of 3263 scans, a validation set of 408 scans, and a testing set of 408 scans. And compare with existing mainstream classification methods, such as CovNet, CBAM, ResNet, etc. Simultaneously compare the visualization results with visualization methods such as CAM. Model performance was evaluated using the Area Under the ROC Curve(AUC), precision, and F1-score. The NL-RAN achieved the AUC of 0.9903, the precision of 0.9473, and the F1-score of 0.9462, surpass all the classification methods compared. The heat map output by the attention module is also clearer than the heat map output by CAM. Our experimental results indicate that our proposed method performs significantly better than existing methods. In addition, the first attention module outputs a heat map containing detailed outline information to increase the interpretability of the model. Our experiments indicate that the inference of our model is fast. It can provide real-time assistance with diagnosis.
