提升儿科X光片降噪效果，保留关键解剖细节以辅助诊断
SharpXR: Structure-Aware Denoising for Pediatric Chest X-Rays
- 采用双解码器U-Net结构，结合边缘保持与可学习融合模块
- 在低剂量图像上实现92.5%肺炎分类准确率，较原图提升3.7个百分点
- 专为资源有限环境设计，适合儿科影像医生和基层医疗机构使用
儿科胸部X光对早期诊断至关重要，尤其在缺乏先进影像设备的资源匮乏地区。低剂量扫描虽降低辐射风险，但引入显著噪声，掩盖重要解剖结构。传统去噪方法常导致细节模糊，影响诊断准确性。本文提出SharpXR，一种结构感知的双解码器U-Net，可在保留关键解剖特征的同时有效去除低剂量儿科X光图像噪声。通过在儿童肺炎胸部X光数据集上模拟真实泊松-高斯噪声，解决成对训练数据稀缺问题。SharpXR在所有评估指标上均优于现有方法，且计算效率高，适用于资源受限场景。经去噪后，下游肺炎分类准确率从88.8%提升至92.5%，凸显其在儿科医疗中的临床价值。
原文摘要 · Abstract (English)
Pediatric chest X-ray imaging is essential for early diagnosis, particularly in low-resource settings where advanced imaging modalities are often inaccessible. Low-dose protocols reduce radiation exposure in children but introduce substantial noise that can obscure critical anatomical details. Conventional denoising methods often degrade fine details, compromising diagnostic accuracy. In this paper, we present SharpXR, a structure-aware dual-decoder U-Net designed to denoise low-dose pediatric X-rays while preserving diagnostically relevant features. SharpXR combines a Laplacian-guided edge-preserving decoder with a learnable fusion module that adaptively balances noise suppression and structural detail retention. To address the scarcity of paired training data, we simulate realistic Poisson-Gaussian noise on the Pediatric Pneumonia Chest X-ray dataset. SharpXR outperforms state-of-the-art baselines across all evaluation metrics while maintaining computational efficiency suitable for resource-constrained settings. SharpXR-denoised images improved downstream pneumonia classification accuracy from 88.8% to 92.5%, underscoring its diagnostic value in low-resource pediatric care.
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