无需真实干净图像，就能保留关键结构的OCT图像去噪方法
Contextual Checkerboard Denoise -- A Novel Neural Network-Based Approach for Classification-Aware OCT Image Denoising
- 基于噪声图像自学习，不依赖真实干净图
- 去噪后图像更清晰，分类准确率显著提升
- 适合眼科OCT图像处理，兼顾视觉质量与诊断需求
与非医学图像去噪以提升图像清晰度为主不同，医学图像去噪需在不引入新伪影的前提下保留关键特征。然而，许多提升清晰度的方法会无意中改变重要信息，影响分类性能和诊断质量。此外，监督式去噪在医学领域难实施，因难以获取真实干净的图像作为标签。本文提出一种新型神经网络方法——上下文棋盘去噪（Contextual Checkerboard Denoising），仅需噪声图像数据集即可学习去噪，同时保留对图像分类至关重要的解剖细节。我们在真实光学相干断层扫描（OCT）图像上进行实验，结果表明该方法显著提升图像质量，使图像更清晰、细节更丰富，同时提高诊断准确率。
原文摘要 · Abstract (English)
In contrast to non-medical image denoising, where enhancing image clarity is the primary goal, medical image denoising warrants preservation of crucial features without introduction of new artifacts. However, many denoising methods that improve the clarity of the image, inadvertently alter critical information of the denoised images, potentially compromising classification performance and diagnostic quality. Additionally, supervised denoising methods are not very practical in medical image domain, since a \emph{ground truth} denoised version of a noisy medical image is often extremely challenging to obtain. In this paper, we tackle both of these problems by introducing a novel neural network based method -- \emph{Contextual Checkerboard Denoising}, that can learn denoising from only a dataset of noisy images, while preserving crucial anatomical details necessary for image classification/analysis. We perform our experimentation on real Optical Coherence Tomography (OCT) images, and empirically demonstrate that our proposed method significantly improves image quality, providing clearer and more detailed OCT images, while enhancing diagnostic accuracy.
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