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半监督GAN与混合规范化和进化超参数调整,以准确检测黑色素瘤

Alireza Golkarieh¹, Parsa Razmara², Ahmadreza Lagzian³

¹Department of Mechanical Engineering, University of Michigan, Michigan, USA.

Scientific reports

|August 30, 2025

概括

这项研究引入了一种改进的半监督生成对抗网络 (SS-GAN),用于准确检测黑色素瘤,并通过基于相互学习的人工蜂群 (ML-ABC) 算法优化超参数,以更好地早期诊断.

科学领域:

人工智能
医学成像
计算生物学

背景情况:

黑色素瘤的诊断依赖于早期检测,而传统的监督学习方法的局限性往往需要大量的标记数据.
对黑色素瘤的监督学习模型对超参数设置敏感,影响诊断准确性.
传统的半监督生成对抗网络 (SS-GAN) 面临模式崩和不良泛化等挑战.

研究的目的:

使用半监督生成对抗网络 (SS-GAN) 开发一种增强的黑色素瘤检测诊断模型.
通过解决模式崩,全球依赖模型和伪标签可靠性来提高SS-GAN的准确性和概括性.
优化模型超参数使用增强的人工蜂群 (ABC) 算法进行强大的黑色素瘤分类.

主要方法:

实现了新的SS-GAN架构,在发电机和区分器中结合了重建损失和自我注意机制.
应用一致性规范化和可靠的伪标记策略,以提高与未标记数据的区分器稳定性和训练.
使用基于相互学习的人工蜂群 (ML-ABC) 算法进行超参数优化,减少对手动调整的依赖.

主要成果:

该模型在四个基准数据集中实现了高F值:ISIC-2020 (92.77%),HAM10000 (93.38%),PH2 (90.63%) 和DermNet (92.62%).
在识别黑色素瘤与非黑色素瘤图像方面显著改善,特别是在有限的标记数据条件下.
增强的SS-GAN有效地解决了模式崩,并通过自我注意和重建损失改善了特征表示.

结论:

开发的ML-ABC优化SS-GAN为黑色素瘤图像分类提供了强大而准确的方法,特别是在稀缺的标记数据的情况下.
拟议的架构改进和优化策略提高了医疗诊断中的生成对抗网络的可靠性和性能.
这项研究为早期发现黑色素瘤提供了有价值的工具,有助于改善患者的治疗结果和疗效.

关键词:

人工蜂群产生性对抗网络超参数优化检测黑色素瘤半监督学习

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