Pediatric pneumonia remains a significant global threat, posing a larger mortality risk than any other communicable disease. According to UNICEF, it is a leading cause of mortality in children under five and requires prompt diagnosis. Early diagnosis using chest radiographs is the prevalent standard, but limitations include low radiation levels in unprocessed images and data imbalance issues. This necessitates the development of efficient, computer-aided diagnosis techniques. To this end, we propose a novel EXplainable Contrastive-based Dilated Convolutional Network with Transformer (XCCNet) for pediatric pneumonia detection. XCCNet harnesses the spatial power of dilated convolutions and the global insights from contrastive-based transformers for effective feature refinement. A robust chest X-ray processing module tackles low-intensity radiographs, while adversarial-based data augmentation mitigates the skewed distribution of chest X-rays in the dataset. Furthermore, we actively integrate an explainability approach through feature visualization, directly aligning it with the attention region that pinpoints the presence of pneumonia or normality in radiographs. The efficacy of XCCNet is comprehensively assessed on four publicly available datasets. Extensive performance evaluation demonstrates the superiority of XCCNet compared to state-of-the-art methods.

该研究针对儿童肺炎检测中的早期诊断问题，特别是胸部X光图像处理中的低强度问题和数据不平衡问题，提出了一种新型的可解释的对比扩张卷积网络（XCCNet）。该方法结合了扩张卷积的空间特性和基于对比变压器的全局洞察，通过有效的特征优化和解释可视化，显著提高了儿童肺炎检测的准确性和可靠性。

可解释的基于对比的扩张卷积网络与变压器用于儿童肺炎检测