用可解释的深度学习提升X光片明暗一致性，减少医生调图时间。
Quality Enhancement of Radiographic X-ray Images by Interpretable Mapping
- 基于临床调图流程设计可解释映射模型，自动优化全局与局部明暗对比
- 在临床数据上实现24.75 dB PSNR和0.8431 SSIM，显著提升图像质量
- 生成像素级解释图，帮助医生理解增强逻辑，适合医学影像辅助系统
X光成像是最常用的医学影像方式，但因患者体位、体型及扫描协议差异，常出现图像明暗和对比度不一致的问题，需临床专家手动调整，耗时费力。现有基于深度学习的端到端方法虽能有效修正，但缺乏可解释性，难以被医生信任。本文提出一种受临床调图流程启发的可解释映射方法，能自动优化图像全局与局部亮度和对比度。该模型生成可解释的像素级映射图，揭示增强决策依据。在临床数据集上的实验表明，该方法可实现24.75 dB的PSNR和0.8431的SSIM，有效提升图像一致性与质量。
原文摘要 · Abstract (English)
X-ray imaging is the most widely used medical imaging modality. However, in the common practice, inconsistency in the initial presentation of X-ray images is a common complaint by radiologists. Different patient positions, patient habitus and scanning protocols can lead to differences in image presentations, e.g., differences in brightness and contrast globally or regionally. To compensate for this, additional work will be executed by clinical experts to adjust the images to the desired presentation, which can be time-consuming. Existing deep-learning-based end-to-end solutions can automatically correct images with promising performances. Nevertheless, these methods are hard to be interpreted and difficult to be understood by clinical experts. In this manuscript, a novel interpretable mapping method by deep learning is proposed, which automatically enhances the image brightness and contrast globally and locally. Meanwhile, because the model is inspired by the workflow of the brightness and contrast manipulation, it can provide interpretable pixel maps for explaining the motivation of image enhancement. The experiment on the clinical datasets show the proposed method can provide consistent brightness and contrast correction on X-ray images with accuracy of 24.75 dB PSNR and 0.8431 SSIM.
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