PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity

Abstract

To use artificial intelligence to assist in diagnoses applications, a model to utilize quality data is required, which results in massive time and cost. In medical data, data imbalance occurs because the amount of data with lesions is less than that without lesions. To overcome this limitation, this study proposes a progressive growth of generative adversarial networks (PGGAN)-based anomaly classification on chest Xrays using weighted multi-scale similarity. An anomaly detection method is applied to learn the distribution of normal images to solve the problem of data imbalance. The use of PGGAN, which is a model that generates high-resolution images by gradually adding layers, enables to find image characteristics on a multi-scale and define the similarity between an original image and a generated image. The anomaly score is calculated by applying the weighted arithmetic mean to a resolution-by-resolution similarity. The threshold is defined after the analysis of the F1-score, and then the classification performance is evaluated. The accuracy of the proposed model was assessed using a confusion matrix and compared with that of a conventional classification model, and the efficiency was demonstrated through ablation studies. The classification accuracy of the test dataset was 0.8525. Compared to a U-net-based disease classifier with low-resolution which accuracy was 0.8410, the performance of the proposed model was 0.8507, exhibited an improvement.

CutPaste-Based Anomaly Detection Model using Multi Scale Feature Extraction in Time Series Streaming Data

“…An anomaly detection model based on data features analyzes data features first, and then detects the data with different features as anomaly. Models capable of analyzing data features well, such as Auto encoder [17], GAN [18], and CNN models are mainly used, even though they are not for anomaly detection. Therefore, there are anomaly detection models based on GAN, such as AnoGAN [19] and GANomaly [20].…”