Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks

TL;DR

This paper introduces a novel anomaly detection method using Cycle-GANs that effectively distinguishes normal and abnormal images in industrial and medical datasets, achieving high accuracy with zero false negatives.

Contribution

The paper proposes a cycle-consistent adversarial network approach that leverages both normal and abnormal data for improved unsupervised anomaly detection.

Findings

Achieves accurate anomaly detection with zero false negatives.

Outperforms state-of-the-art methods on industrial and medical datasets.

Utilizes cycle-consistent GANs for effective image translation and anomaly scoring.

Abstract

In this study, a new Anomaly Detection (AD) approach for industrial and medical images is proposed. This method leverages the theoretical strengths of unsupervised learning and the data availability of both normal and abnormal classes. Indeed, the AD is often formulated as an unsupervised task, implying only normal images during training. These normal images are devoted to be reconstructed, through an autoencoder architecture for instance. However, the information contained in abnormal data, when available, is also valuable for this reconstruction. The model would be able to identify its weaknesses by better learning how to transform an abnormal (respectively normal) image into a normal (respectively abnormal) one, helping the entire model to learn better than a single normal to normal reconstruction. To address this challenge, the proposed method uses Cycle-Generative Adversarial Networks (Cycle-GAN) for (ab)normal-to-normal translation. After an input image has been reconstructed by the normal generator, an anomaly score quantifies the differences between the input and its reconstruction. Based on a threshold set to satisfy a business quality constraint, the input image is then flagged as normal or not. The proposed method is evaluated on industrial and medical datasets. The results demonstrate accurate performance with a zero false negative constraint compared to state-of-the-art methods. The code is available at https://github.com/ValDelch/CycleGANS-AnomalyDetection.