Automated anomaly detection in histology images using deep learning

Lillie Shelton; Rajath Soans; Tosha Shah; Thomas Forest; Kyathanahalli Janardhan; Michael Napolitano; Raymond Gonzalez; Grady Carlson; Jyoti K. Shah; Antong Chen

doi:10.1117/12.3006224

3 April 2024 Automated anomaly detection in histology images using deep learning

Lillie Shelton, Rajath Soans, Tosha Shah, Thomas Forest, Kyathanahalli Janardhan, Michael Napolitano, Raymond Gonzalez, Grady Carlson, Jyoti K. Shah, Antong Chen

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129330N (2024) https://doi.org/10.1117/12.3006224
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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%.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lillie Shelton, Rajath Soans, Tosha Shah, Thomas Forest, Kyathanahalli Janardhan, Michael Napolitano, Raymond Gonzalez, Grady Carlson, Jyoti K. Shah, and Antong Chen "Automated anomaly detection in histology images using deep learning", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129330N (3 April 2024); https://doi.org/10.1117/12.3006224

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