CT-based Anomaly Detection of Liver Tumors Using Generative Diffusion Prior

TL;DR

This paper introduces a novel CT-based liver tumor anomaly detection method using generative diffusion priors, significantly improving detection accuracy by inpainting and discrepancy analysis.

Contribution

It presents a new approach employing generative diffusion priors for liver anomaly detection, outperforming existing methods in accuracy.

Findings

7.9% increase in AUC over state-of-the-art methods

Effective inpainting reduces false positives

Demonstrated on two liver CT datasets

Abstract

CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify pathological structures. However, these methods may produce suboptimal results, overlooking subtle differences among various tissue types. To address this challenge, here we employ generative diffusion prior to inpaint the liver as the reference facilitating anomaly detection. Specifically, we use an adaptive threshold to extract a mask of abnormal regions, which are then inpainted using a diffusion prior to calculating an anomaly score based on the discrepancy between the original CT image and the inpainted counterpart. Our methodology has been tested on two liver CT datasets, demonstrating a significant improvement in detection accuracy, with a 7.9% boost in the area under the curve (AUC) compared to the state-of-the-art. This performance gain underscores the potential of our approach to refine the radiological assessment of liver diseases.