利用基于 GAN 的异常检测推进医学成像技术的发展

Q2 Mathematics
Nabila Ounasser, Maryem Rhanoui, M. Mikram, B. El Asri
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引用次数: 0

摘要

医学成像中的异常检测是一项复杂的挑战,有限的注释数据更加剧了这一挑战。生成式对抗网络(GAN)的最新进展提供了潜在的解决方案,但其在医学成像中的有效性在很大程度上仍是未知数。我们对基于生成式对抗网络的异常检测技术的优势和制约因素进行了有针对性的探索。我们的研究包括在代表不同模式和器官/组织类型的三个医学成像数据集上采用八种异常检测方法进行实验。这些实验产生了明显不同的结果。结果表现出很大的差异性,指标跨度很大(曲线下面积 (AUC),0.475-0.991):0.475-0.991;灵敏度:0.17-0.98;特异性:0.14-0.97)。此外,我们还为在医学成像中实施异常检测模型提供了指导,并预测了未来研究的关键途径。结果揭示了受数据集大小、异常微妙性和分散性等因素影响的不同性能。我们的研究结果为医学成像中异常检测的复杂情况提供了见解,为未来的研究和部署提供了建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advancing medical imaging with GAN-based anomaly detection
Anomaly detection in medical imaging is a complex challenge, exacerbated by limited annotated data. Recent advancements in generative adversarial networks (GANs) offer potential solutions, yet their effectiveness in medical imaging remains largely uncharted. We conducted a targeted exploration of the benefits and constraints associated with GAN-based anomaly detection techniques. Our investigations encompassed experiments employing eight anomaly detection methods on three medical imaging datasets representing diverse modalities and organ/tissue types. These experiments yielded notably diverse results. The results exhibited significant variability, with metrics spanning a wide range (area under the curve (AUC): 0.475-0.991; sensitivity: 0.17-0.98; specificity: 0.14-0.97). Furthermore, we offer guidance for implementing anomaly detection models in medical imaging and anticipate pivotal avenues for future research. Results unveil varying performances, influenced by factors like dataset size, anomaly subtlety, and dispersion. Our findings provide insights into the complex landscape of anomaly detection in medical imaging, offering recommendations for future research and deployment.
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
782
期刊介绍: The aim of Indonesian Journal of Electrical Engineering and Computer Science (formerly TELKOMNIKA Indonesian Journal of Electrical Engineering) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the applications of Telecommunication and Information Technology, Applied Computing and Computer, Instrumentation and Control, Electrical (Power), Electronics Engineering and Informatics which covers, but not limited to, the following scope: Signal Processing[...] Electronics[...] Electrical[...] Telecommunication[...] Instrumentation & Control[...] Computing and Informatics[...]
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