用扩散模型自监督去噪，保留医学图像关键细节。
DiffDenoise: Self-Supervised Medical Image Denoising with Conditional Diffusion Models
- 用预训练盲区网络作为条件，训练扩散模型去噪。
- 通过双向噪声平均采样，提升去噪稳定性和细节保留。
- 适合需要高保真图像的医学影像分析场景。
近年来，自监督去噪方法被广泛提出，但普遍存在过度平滑问题，导致医学图像中关键细结构丢失。本文提出 DiffDenoise，一种专为医学图像设计的自监督去噪方法，旨在保留高频细节。该方法包含三个阶段：首先，使用预训练盲区网络（Blind-Spot Network）输出作为条件，训练扩散模型于含噪图像；其次，引入一种新型稳定化反向采样技术，通过一对对称噪声初始化并平均采样结果，生成清晰图像；最后，利用扩散模型生成的去噪图像与原始噪声图配对，训练监督式去噪网络。实验表明，DiffDenoise 在合成与真实世界医学图像去噪任务中均优于现有最先进方法。本研究提供了理论基础与实践洞见，验证了该方法在多种医学成像模态及解剖结构上的有效性。
原文摘要 · Abstract (English)
Many self-supervised denoising approaches have been proposed in recent years. However, these methods tend to overly smooth images, resulting in the loss of fine structures that are essential for medical applications. In this paper, we propose DiffDenoise, a powerful self-supervised denoising approach tailored for medical images, designed to preserve high-frequency details. Our approach comprises three stages. First, we train a diffusion model on noisy images, using the outputs of a pretrained Blind-Spot Network as conditioning inputs. Next, we introduce a novel stabilized reverse sampling technique, which generates clean images by averaging diffusion sampling outputs initialized with a pair of symmetric noises. Finally, we train a supervised denoising network using noisy images paired with the denoised outputs generated by the diffusion model. Our results demonstrate that DiffDenoise outperforms existing state-of-the-art methods in both synthetic and real-world medical image denoising tasks. We provide both a theoretical foundation and practical insights, demonstrating the method's effectiveness across various medical imaging modalities and anatomical structures.
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