皮肤病学的生成对抗网络:对当前应用,挑战和未来前景的叙述性审查
Rosa Maria Izu-Belloso1, Rafael Ibarrola-Altuna2, Alex Rodriguez-Alonso3
1Medicine Faculty, Hospital Universitario Basurto, 48013 Bilbao, Spain.
Bioengineering (Basel, Switzerland)
|October 29, 2025
概括
生成对抗网络 (GANs) 正在通过实现合成图像生成和改进诊断模型来改变皮肤病学. 虽然有希望,但需要进一步验证和标准化,才能使这些人工智能工具在临床上得到广泛采用.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 皮肤病学 皮肤病学
背景情况:
- 生成对抗网络 (GAN) 在人工智能和医学成像方面越来越重要.
- 在皮肤病学中,GAN为图像分析,合成数据生成和诊断模型增强提供解决方案.
研究的目的:
- 审查GAN在皮肤病学中的应用.
- 为了确定临床用例,分析GAN架构,并评估性能指标.
- 讨论皮肤病学实践中GAN的挑战,伦理问题和未来方向.
主要方法:
- 对27项关于皮肤病学中GAN的关键研究进行系统叙事审查.
- 对GAN架构进行分析 (例如DCGAN,cGAN,StyleGAN,CycleGAN).
- 对绩效指标的评估 (例如,FID,SSIM,初始分数,子系数).
主要成果:
- 确定了GANs在皮肤病学中的11个主要临床用例,包括合成表型生成,细分,denoising和超分辨率.
- 对比了各种GAN架构和技术评估指标.
- 突出了诸如数据不平衡,过度装配和缺乏临床验证等挑战,以及伦理和监管方面的考虑.
结论:
- 在皮肤病方面,GAN具有变革性的潜力,为图像分析和诊断提供了新的方法.
- 标准化协议和严格的临床验证对于将GAN纳入实践至关重要.
- 人工智能工具和开放访问数据集的民主化对于公平的皮肤病护理至关重要.
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