Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Abiy Abinet Mamo, Bealu Girma Gebresilassie, Aniruddha Mukherjee, Vikas Hassija, Vinay Chamola
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Abstract

In medical imaging, traditional methods have long been relied upon. However, the integration of Generative Adversarial Networks (GANs) has sparked a paradigm shift, ushering in a new era of innovation. Our comprehensive investigation explores the groundbreaking impact of GANs on medical imaging, examining the evolution from traditional techniques to GAN-driven approaches. Through meticulous analysis, we dissect various aspects of GANs, encompassing their taxonomy, historical progression, and diverse iterations such as Self-Attention GANs (SAGAN), Conditional GANs, and Progressive Growing GANs (PGGAN). Complemented by a practical case study, we scrutinize the extensive applications of GANs, spanning image generation, reconstruction, enhancement, segmentation, and super-resolution. Despite promising prospects, enduring challenges including data scarcity, interpretability issues, and ethical concerns persist. Looking ahead, we anticipate advancements in personalized and pathological image generation, cross-modal synthesis, real-time interactive image generation, and enhanced anomaly detection. Through this review, we underscore the transformative potential of GANs in reshaping medical imaging practices, while also outlining avenues for future research endeavors.

Abstract Image

通过生成式对抗网络推进医学成像:全面回顾与未来展望
长期以来,医学成像一直依赖传统方法。然而,生成对抗网络(GANs)的集成引发了范式的转变,开创了创新的新时代。我们的全面调查探讨了生成式对抗网络对医学成像的突破性影响,研究了从传统技术到生成式对抗网络驱动方法的演变过程。通过细致的分析,我们剖析了 GANs 的各个方面,包括其分类、历史进程和各种迭代,如自注意 GANs (SAGAN)、条件 GANs 和渐进生长 GANs (PGGAN)。在实际案例研究的补充下,我们仔细研究了 GANs 的广泛应用,包括图像生成、重建、增强、分割和超分辨率。尽管前景广阔,但包括数据稀缺、可解释性问题和伦理问题在内的持久挑战依然存在。展望未来,我们预计在个性化和病理图像生成、跨模态合成、实时交互式图像生成和增强型异常检测方面将取得进展。通过这篇综述,我们强调了 GANs 在重塑医学影像实践方面的变革潜力,同时也为未来的研究工作勾勒了蓝图。
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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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