Unsupervised Cellular Anomaly Detection in Toxicological Histopathology
Keywords: Anomaly Detection, Out-of-distribution Detection, Toxicology, Histopathology, Foundation Models, Cellular Analysis, Drug Safety Assessment
TL;DR: We evaluate cellular anomaly detection for toxicological histopathology, and find that KNN-distance based method using best-performing foundation models outperforms state-of-the-art methods including DDPMs.
Abstract: Irregularities in cellular representation play a crucial role in assessing drug-induced tissue alterations in toxicological histopathology studies. However, the process of annotating rare abnormal cellular variations for training supervised deep learning models presents significant challenges and lacks scalability. While anomaly detection is well-suited for this purpose, it has not yet been explored for cellular-level analysis. In this study, we evaluate cellular anomaly detection using datasets derived from the kidneys and livers of Wistar rats. Our findings indicate that a KNN-distance-based anomaly detection method significantly benefits from employing a feature extractor that has been pre-trained on extensive unsupervised histopathology datasets. When utilizing the best-performing feature extractor, the KNN-distance method surpasses state-of-the-art anomaly detection models by over 4.84% (AUC), including the denoising diffusion probabilistic model, in detecting cellular anomalies. Additionally, we assess the effectiveness of this method in identifying variations in anomalous cell counts between control and treated animal tissues within a toxicological study, revealing a statistically significant difference between the two dosage groups.
Primary Subject Area: Unsupervised Learning and Representation Learning
Secondary Subject Area: Application: Histopathology
Paper Type: Validation or Application
Registration Requirement: Yes
Visa & Travel: Yes
Submission Number: 145
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