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使用生成对抗网络和近接政策优化进行黑色素瘤分类
Xiangui Ju1,2, Chi-Ho Lin2, Suan Lee2
1Beijing Jinzhituo Technology Co., Ltd., Beijing, China.
Photochemistry and photobiology
|July 31, 2024
概括
本研究提出了用于黑色素瘤分类的新深度学习框架,利用政策之外的近接政策优化 (PPO) 和生成对抗网络 (GAN) 来提高准确性并处理早期癌症检测的不平衡数据.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 黑色素瘤是一种严重的癌症,通常是由紫外线辐射引起的,由于其具有攻击性,需要早期检测.
- 当前的分类方法面临着不平衡的数据集和实现高精度的挑战.
研究的目的:
- 开发和验证用于增强黑色素瘤分类的新型深度学习框架.
- 解决数据不平衡问题,提高黑色素瘤检测中的模型通用性.
主要方法:
- 使用了深度学习框架,用于特征提取的三个扩展卷积层.
- 整合了政策之外的近距离政策优化 (PPO),以管理不平衡的培训数据.
- 采用了一种具有数据增强和稳定训练的新型规范化的生成对抗网络 (GAN).
主要成果:
- 获得了91.836%的F测量和91.920%的几何平均值.
- 与现有模型相比,表现出优越的性能.
- 在SIIM-ISIC黑色素瘤分类挑战-ISIC-2020数据集上得到验证.
结论:
- 拟议的框架显示了改善早期黑色素瘤检测的巨大潜力.
- 该模型的性能表明,在临床环境中,它可以用于更准确的治疗规划.
- 人工智能驱动的诊断技术的进步可以显著帮助打击黑色素瘤等侵袭性癌症.
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