通过渐进式掩码优化，提升低剂量CT去噪的自监督学习效果。
Progressive Masked Refinement Self-supervised Learning for Low-Dose CT Denoising
- 分步掩码机制逐步逼近真实噪声特征，增强学习精度。
- 引入高斯与泊松噪声组合，防止模型学成恒等映射。
- 在Mayo数据集上超越多数自监督方法，媲美有监督模型。
自监督学习被广泛研究用于低剂量计算机断层扫描（LDCT）图像去噪，因其可减少对难以获取的配对正常剂量CT（NDCT）数据的依赖。然而，现有自监督盲区去噪方法可能因仅在掩码区域评估损失而未能充分利用像素级监督信息。为此，本文提出一种新颖的渐进式掩码精炼学习框架，逐步优化去噪结果并保留与利用可用的LDCT信息。具体而言，训练中显式注入受控的高斯与泊松噪声以正则化去噪过程，缓解平凡恒等映射问题；同时引入分步掩码去噪机制，逐步缩小合成退化与真实LDCT噪声特征之间的差距，实现更精细的去噪学习。在Mayo LDCT数据集上的大量实验表明，所提方法优于现有自监督方法，并达到或超过多个代表性有监督去噪方法的性能。
原文摘要 · Abstract (English)
Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on paired normal-dose CT (NDCT) data, which are often difficult to obtain. However, many existing self-supervised blind-spot denoising methods may under-utilize pixel-wise supervisory information loss due to evaluating the training loss only at masked locations. To mitigate this issue, we propose a novel Progressive Masked Refinement Learning framework that progressively refines denoising results while preserving and exploiting available LDCT information. Specifically, we explicitly inject a combination of controlled Gaussian and Poisson noise during training to regularize the denoising process and mitigate trivial identity mapping. Furthermore, we introduce a step-wise mask denoising mechanism that gradually reduces the discrepancy between synthetic corruption and the noise characteristics of LDCT images, enabling more fine-grained learning for denoising. Extensive experiments on the Mayo LDCT dataset demonstrate that the proposed method outperforms existing self-supervised approaches and achieves performance comparable to, or better than, several representative supervised denoising methods.
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