Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays

TL;DR

This paper introduces DDAD, a novel anomaly detection method for chest X-rays that leverages both known normal and unlabeled images, outperforming existing one-class models by capturing more comprehensive anomaly features.

Contribution

The paper proposes a dual-distribution discrepancy approach that utilizes unlabeled images alongside normal images, addressing limitations of one-class models in CXR anomaly detection.

Findings

DDAD outperforms state-of-the-art methods on three CXR datasets.

The dual-distribution discrepancy effectively captures anomalies.

Significant improvements in anomaly detection accuracy.

Abstract

Chest X-ray (CXR) is the most typical radiological exam for diagnosis of various diseases. Due to the expensive and time-consuming annotations, detecting anomalies in CXRs in an unsupervised fashion is very promising. However, almost all of the existing methods consider anomaly detection as a one-class classification (OCC) problem. They model the distribution of only known normal images during training and identify the samples not conforming to normal profile as anomalies in the testing phase. A large number of unlabeled images containing anomalies are thus ignored in the training phase, although they are easy to obtain in clinical practice. In this paper, we propose a novel strategy, Dual-distribution Discrepancy for Anomaly Detection (DDAD), utilizing both known normal images and unlabeled images. The proposed method consists of two modules. During training, one module takes both known normal and unlabeled images as inputs, capturing anomalous features from unlabeled images in some way, while the other one models the distribution of only known normal images. Subsequently, inter-discrepancy between the two modules, and intra-discrepancy inside the module that is trained on only normal images are designed as anomaly scores to indicate anomalies. Experiments on three CXR datasets demonstrate that the proposed DDAD achieves consistent, significant gains and outperforms state-of-the-art methods. Code is available at https://github.com/caiyu6666/DDAD.