CS-GANomaly: A Supervised Anomaly Detection Approach with Ancillary Classifier GANs for Chromosome Images

Although anomaly detection is an urgent and significant task in various applications, there are few good enough methods to meet the requirements. The challenge of anomaly detection lies in how to distinguish the diverse or even unknown anomalies. Therefore, previous studies have focused on imagelevel anomaly detection tasks, and mostly based on unsupervised methods. These methods only consider the characteristics of normal samples and are confined to the “normal” category, ignoring information between samples. In particular, the limitations of unsupervised methods are more obvious for pixellevel abnormalities such as the chromosomal structural anomalies investigated in this paper, because the similarity between normal and abnormal samples is relatively high. In this work, we propose a supervised anomaly detection approach Classification - Enhanced GANomaly (abbreviated as CS-GANomaly). By fusing the category information with the sample, the spatial distribution of the sample has regional characteristics and then combining the anomaly detection strategies to carry out multidimension anomaly detection. The CS-GANomaly adopts the GAN framework for adversarial training and adapts the GAN output to anomaly detection. The experimental results prove the superiority of our approach, whether it is a pixel-level anomaly in abnormal chromosome dataset or image-level anomaly in the benchmark dataset.