Evaluation Metric for Quality Control and Generative Models in Histopathology Images
This work addresses a domain-specific problem for histopathology researchers by offering an incremental improvement in evaluation metrics for image generation and quality control.
The study tackled the problem of evaluating generative models and image quality in histopathology by introducing ResNet-L2 (RL2), a novel metric that provides reliable assessments across diverse datasets, with advantages such as being lighter, faster, and requiring fewer images for stable values compared to traditional metrics like FID.
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.