Automated anomaly detection in histology images using deep learning

In this study, we have developed a method to detect anomalies in histology slides containing tissues sourced from multiple organs of rats. In the nonclinical phase of drug development, candidate drugs are typically tested on animals such as rats, and a postmortem assessment is conducted based on human evaluation of histology slides. Findings in those histology slides manifest as anomalous departures from expectation on Whole Slide Images (WSIs). Our proposed method, makes use of a StyleGAN2 and ResNet based encoder to identify anomalies in WSIs. Using these models, we train an image reconstruction pipeline only on an anomaly-free ('normal') dataset. We then use this pipeline to identify anomalies using the reconstruction quality measured by Structural Similarity Index (SSIM). Our experiments were carried out on 54 WSIs across 40 different organ types and achieved a patch-level classification accuracy of 88%.