提出轻量级图像质量评估指标RL2，专为病理图像生成设计。
Evaluation Metric for Quality Control and Generative Models in Histopathology Images
- 基于ResNet特征与归一化流，在潜在空间计算RMSE距离
- 对模糊、噪声等退化类型响应单调，评估结果稳定可靠
- 速度快、内存少，适合低数据量场景与切片去劣质块
本研究提出ResNet-L2（RL2）新型评估指标，用于病理图像生成模型与图像质量评估，克服传统指标（如FID）在数据稀缺时的局限性。RL2利用ResNet特征结合归一化流，在潜在空间计算均方根误差（RMSE），可在多种病理图像数据集上提供可靠评估。我们测试了其在模糊、高斯噪声、椒盐噪声、矩形遮挡及扩散过程等退化类型下的表现，结果显示其随退化程度增加呈单调下降趋势，适用于图像质量评估。此外，该方法可高效剔除全幻灯片扫描中低质量区域，且相比传统指标更轻量快速，所需样本数更少即可获得稳定值。
原文摘要 · Abstract (English)
Our study introduces ResNet-L2 (RL2), a novel metric for evaluating generative models and image quality in histopathology, addressing limitations of traditional metrics, such as Frechet inception distance (FID), when the data is scarce. RL2 leverages ResNet features with a normalizing flow to calculate RMSE distance in the latent space, providing reliable assessments across diverse histopathology datasets. We evaluated the performance of RL2 on degradation types, such as blur, Gaussian noise, salt-and-pepper noise, and rectangular patches, as well as diffusion processes. RL2's monotonic response to increasing degradation makes it well-suited for models that assess image quality, proving a valuable advancement for evaluating image generation techniques in histopathology. It can also be used to discard low-quality patches while sampling from a whole slide image. It is also significantly lighter and faster compared to traditional metrics and requires fewer images to give stable metric value.
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