一种面向小儿肺炎检测的可解释对比学习扩张卷积网络与Transformer
An Explainable Contrastive-based Dilated Convolutional Network with Transformer for Pediatric Pneumonia Detection
- Indian Institute of Technology (IIT) Indore（印度理工学院印多尔分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出XCCNet，结合扩张卷积与对比学习Transformer，并引入胸部X光处理模块、对抗数据增强和特征可视化可解释性，在四个公开数据集上验证了其优于现有方法的小儿肺炎检测性能。
AI中文摘要：
小儿肺炎仍然是一个重大的全球性威胁，其死亡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，它是五岁以下儿童死亡的主要原因，需要及时诊断。使用胸片进行早期诊断是普遍的标准，但存在未处理图像辐射水平低以及数据不平衡问题等局限性。这需要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的扩张卷积网络与Transformer（XCCNet），用于小儿肺炎检测。XCCNet利用扩张卷积的空间能力和基于对比学习的Transformer的全局洞察力进行有效的特征细化。一个鲁棒的胸部X光处理模块处理低强度放射影像，而基于对抗的数据增强缓解了数据集中胸部X光片的偏斜分布。此外，我们通过特征可视化积极整合可解释性方法，将其直接与精确定位放射影像中肺炎或正常存在的注意力区域对齐。XCCNet的有效性在四个公开可用的数据集上进行了全面评估。广泛的性能评估表明，与最先进的方法相比，XCCNet具有优越性。
英文摘要：
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.
