Unsupervised Cellular Anomaly Detection in Toxicological Histopathology

Saketh Juturu, Geetank Raipuria, Raghav Amaravadi, Aman Srivastava, Malini Roy, Nitin Singhal
Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, PMLR 301:716-734, 2026.

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 kidney and liver tissue of Wistar rats. Our findings show 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v301-juturu26a, title = {Unsupervised Cellular Anomaly Detection in Toxicological Histopathology}, author = {Juturu, Saketh and Raipuria, Geetank and Amaravadi, Raghav and Srivastava, Aman and Roy, Malini and Singhal, Nitin}, booktitle = {Proceedings of The 8th International Conference on Medical Imaging with Deep Learning}, pages = {716--734}, year = {2026}, editor = {Tasdizen, Tolga and Elhabian, Shireen and Summers, Ronald and Chen, Chen and Koch, Lisa and Zhuang, Yan}, volume = {301}, series = {Proceedings of Machine Learning Research}, month = {09--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v301/main/assets/juturu26a/juturu26a.pdf}, url = {https://proceedings.mlr.press/v301/juturu26a.html}, 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 kidney and liver tissue of Wistar rats. Our findings show 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.} }
Endnote
%0 Conference Paper %T Unsupervised Cellular Anomaly Detection in Toxicological Histopathology %A Saketh Juturu %A Geetank Raipuria %A Raghav Amaravadi %A Aman Srivastava %A Malini Roy %A Nitin Singhal %B Proceedings of The 8th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Tolga Tasdizen %E Shireen Elhabian %E Ronald Summers %E Chen Chen %E Lisa Koch %E Yan Zhuang %F pmlr-v301-juturu26a %I PMLR %P 716--734 %U https://proceedings.mlr.press/v301/juturu26a.html %V 301 %X 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 kidney and liver tissue of Wistar rats. Our findings show 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.
APA
Juturu, S., Raipuria, G., Amaravadi, R., Srivastava, A., Roy, M. & Singhal, N.. (2026). Unsupervised Cellular Anomaly Detection in Toxicological Histopathology. Proceedings of The 8th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 301:716-734 Available from https://proceedings.mlr.press/v301/juturu26a.html.

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