Yao Dan, Xu Zhenghua, Lin Yi, Zhan Yuefu
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, China.
Department of Radiology, Hainan Women and Children's Medical Center, Haikou, China.
Front Bioeng Biotechnol. 2023 May 17;11:1058888. doi: 10.3389/fbioe.2023.1058888. eCollection 2023.
Computer-aided diagnosis (CAD) methods such as the X-rays-based method is one of the cheapest and safe alternative options to diagnose the disease compared to other alternatives such as Computed Tomography (CT) scan, and so on. However, according to our experiments on X-ray public datasets and real clinical datasets, we found that there are two challenges in the current classification of pneumonia: existing public datasets have been preprocessed too well, making the accuracy of the results relatively high; existing models have weak ability to extract features from the clinical pneumonia X-ray dataset. To solve the dataset problems, we collected a new dataset of pediatric pneumonia with labels obtained through a comprehensive pathogen-radiology-clinical diagnostic screening. Then, to accurately capture the important features in imbalanced data, based on the new dataset, we proposed for the first time a two-stage training multimodal pneumonia classification method combining X-ray images and blood testing data, which improves the image feature extraction ability through a global-local attention module and mitigate the influence of class imbalance data on the results through the two-stage training strategy. In experiments, the performance of our proposed model is the best on new clinical data and outperforms the diagnostic accuracy of four experienced radiologists. Through further research on the performance of various blood testing indicators in the model, we analyzed the conclusions that are helpful for radiologists to diagnose.
基于X射线的计算机辅助诊断（CAD）方法是与计算机断层扫描（CT）等其他诊断方法相比最便宜且安全的疾病诊断替代方案之一。然而，根据我们在X射线公共数据集和真实临床数据集上的实验，我们发现在当前肺炎分类中存在两个挑战：现有的公共数据集预处理得过于完善，使得结果的准确率相对较高；现有模型从临床肺炎X射线数据集中提取特征的能力较弱。为了解决数据集问题，我们通过全面的病原体-放射学-临床诊断筛查收集了一个带有标签的小儿肺炎新数据集。然后，为了准确捕捉不平衡数据中的重要特征，基于新数据集，我们首次提出了一种结合X射线图像和血液检测数据的两阶段训练多模态肺炎分类方法，该方法通过全局-局部注意力模块提高图像特征提取能力，并通过两阶段训练策略减轻类别不平衡数据对结果的影响。在实验中，我们提出的模型在新的临床数据上表现最佳，并且优于四位经验丰富的放射科医生的诊断准确率。通过对模型中各种血液检测指标性能的进一步研究，我们分析了有助于放射科医生进行诊断的结论。
