Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images

TL;DR

This paper introduces Ano-NAViLa, a novel vision-language model that leverages normal and abnormal pathology knowledge to improve anomaly detection and interpretability in pathology images, addressing challenges of data scarcity and variability.

Contribution

It presents a lightweight, knowledge-augmented model specifically designed for pathology anomaly detection, outperforming existing methods and enhancing interpretability.

Findings

Achieves state-of-the-art performance on lymph node datasets

Enhances robustness to variability in pathology images

Provides interpretable image-text associations

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.