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.

翻译：儿童肺炎仍然是全球重大威胁，其致死风险高于任何其他传染性疾病。根据联合国儿童基金会的数据，肺炎是五岁以下儿童死亡的主要原因，需要及时诊断。利用胸部X光片进行早期诊断是当前主流标准，但存在未处理图像辐射水平较低和数据不平衡等局限性。这促使我们开发高效的计算机辅助诊断技术。为此，我们提出了一种新颖的基于可解释对比学习与Transformer的扩张卷积网络（XCCNet）用于儿童肺炎检测。XCCNet利用扩张卷积的空间提取能力和基于对比学习的Transformer的全局洞察力进行有效特征优化。一个鲁棒的胸部X光处理模块处理低强度X光片，而基于对抗学习的数据增强技术缓解了数据集中胸部X光片的分布偏斜问题。此外，我们通过特征可视化主动集成可解释性方法，将其直接与定位X光片中肺炎或正常区域的注意力区域对齐。XCCNet在四个公开数据集上进行了全面效能评估。广泛的性能评估证明了XCCNet相较于现有最先进方法的优越性。