Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images
Abstract
Anomaly detection in computational pathology aims to identify rare and scarce anomalies where disease-related data are often limited or missing. Existing anomaly detection methods, primarily designed for industrial settings, face limitations in pathology due to computational constraints, diverse tissue structures, and lack of interpretability. To address these challenges, we propose Ano-NAViLa, a Normal and Abnormal pathology knowledge-augmented Vision-Language model for Anomaly detection in pathology images. Ano-NAViLa is built on a pre-trained vision-language model with a lightweight trainable MLP. By incorporating both normal and abnormal pathology knowledge, Ano-NAViLa enhances accuracy and robustness to variability in pathology images and provides interpretability through image-text associations. Evaluated on two lymph node datasets from different organs, Ano-NAViLa achieves the state-of-the-art performance in anomaly detection and localization, outperforming competing models.
Keywords
Cite
@article{arxiv.2508.15256,
title = {Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images},
author = {Jinsol Song and Jiamu Wang and Anh Tien Nguyen and Keunho Byeon and Sangjeong Ahn and Sung Hak Lee and Jin Tae Kwak},
journal= {arXiv preprint arXiv:2508.15256},
year = {2025}
}
Comments
Accepted to ICCV 2025. Code is available at: https://github.com/QuIIL/ICCV2025_Ano-NAViLa