使用生成对抗性网络进行高质量的半监督异常检测。

IF 2.3 3区 医学 Q3 ENGINEERING, BIOMEDICAL International Journal of Computer Assisted Radiology and Surgery Pub Date : 2024-11-01 Epub Date: 2023-11-09 DOI:10.1007/s11548-023-03031-9
Yuki Sato, Junya Sato, Noriyuki Tomiyama, Shoji Kido
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引用次数: 0

摘要

目的:在使用生成模型而不是分类模型的异常检测方法中,异常区域的可视化更容易。然而,实现异常检测的准确性和异常区域的清晰可视化是具有挑战性的。本研究旨在建立一种使用生成对抗性网络(GAN)将异常区域的检测精度和清晰可视化相结合的方法。方法:本研究使用具有自适应鉴别器增强的StyleGAN2(StyleGAN2-ADA)作为图像生成模型,该模型可以在有限的数据集数量下生成高分辨率和高质量的图像,并且使用像素到风格到像素(pSp)编码器将图像转换为中间潜在变量。我们结合了现有的训练方法,提出了一种使用中间潜在变量计算异常分数的方法。将这两种方法相结合的方法被称为高质量异常GAN(HQ AnoGAN)。结果:使用三个数据集获得的实验结果表明,HQ AnoGAN具有与现有方法相同或更好的检测精度。使用生成的图像对异常区域进行可视化的结果表明,HQ AnoGAN可以生成比现有方法更自然的图像,并且在异常区域的可视化中定性地更准确。结论:本研究提出了由StyleGAN2-ADA和pSp编码器组成的HQ AnoGAN,并提出了一种最佳异常评分计算方法。实验结果表明,HQ AnoGAN可以实现高的异常检测精度和异常区域的清晰可视化;因此,HQ AnoGAN在需要解释诊断的医学影像诊断病例中显示出巨大的应用潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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High-quality semi-supervised anomaly detection with generative adversarial networks.

Purpose: The visualization of an anomaly area is easier in anomaly detection methods that use generative models rather than classification models. However, achieving both anomaly detection accuracy and a clear visualization of anomalous areas is challenging. This study aimed to establish a method that combines both detection accuracy and clear visualization of anomalous areas using a generative adversarial network (GAN).

Methods: In this study, StyleGAN2 with adaptive discriminator augmentation (StyleGAN2-ADA), which can generate high-resolution and high-quality images with limited number of datasets, was used as the image generation model, and pixel-to-style-to-pixel (pSp) encoder was used to convert images into intermediate latent variables. We combined existing methods for training and proposed a method for calculating anomaly scores using intermediate latent variables. The proposed method, which combines these two methods, is called high-quality anomaly GAN (HQ-AnoGAN).

Results: The experimental results obtained using three datasets demonstrated that HQ-AnoGAN has equal or better detection accuracy than the existing methods. The results of the visualization of abnormal areas using the generated images showed that HQ-AnoGAN could generate more natural images than the existing methods and was qualitatively more accurate in the visualization of abnormal areas.

Conclusion: In this study, HQ-AnoGAN comprising StyleGAN2-ADA and pSp encoder was proposed with an optimal anomaly score calculation method. The experimental results show that HQ-AnoGAN can achieve both high abnormality detection accuracy and clear visualization of abnormal areas; thus, HQ-AnoGAN demonstrates significant potential for application in medical imaging diagnosis cases where an explanation of diagnosis is required.

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来源期刊
International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery ENGINEERING, BIOMEDICAL-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
5.90
自引率
6.70%
发文量
243
审稿时长
6-12 weeks
期刊介绍: The International Journal for Computer Assisted Radiology and Surgery (IJCARS) is a peer-reviewed journal that provides a platform for closing the gap between medical and technical disciplines, and encourages interdisciplinary research and development activities in an international environment.
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