ProGleason-GAN:前列腺癌的条件渐进生长GAN 格里森等级补丁合成
Alejandro Golfe1, Rocío Del Amor1, Adrián Colomer2
1Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano (HUMAN-Tech), Universitat Politècnica de València, 46022, Spain.
Computer methods and programs in biomedicine
|July 2, 2023
概括
这项研究介绍了ProGleason-GAN,这是一个生成的深度学习模型,可以合成前列腺癌组织图像. 这种人工智能工具增强了不平衡的数据集,改善了前列腺癌的诊断分类模型.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能
- 数字病理学数字病理学
背景情况:
- 前列腺癌的诊断依赖于耗时的格里森评分系统.
- 人工智能旨在自动化格里森分级,但由于训练数据不足和不平衡,它面临着挑战.
- 有限的数据通用性阻碍了人工智能模型在前列腺癌分类方面的性能.
研究的目的:
- 开发一种生成的深度学习模型,用于合成前列腺组织病理组织贴片.
- 用合成格里森等级模式来增强不平衡的数据集.
- 评估合成数据对改善前列腺癌分类模型的影响.
主要方法:
- 开发了一个有条件的渐进增长的生成对抗网络 (ProGleason-GAN).
- 格里森等级信息被嵌入用于控制合成样本模式.
- 用小批量标准偏差和像素正常化来提高训练稳定性和性能.
主要成果:
- 采用ProGleason-GAN模型实现了最先进的Frechet初始距离 (FID) 得分.
- 合成非癌症样本,GG3,GG4和GG5模式被生成.
- 该框架在用于数据增强时,在SICAPv2数据集上显示了改进的分类结果.
结论:
- 带有斑点正常化的ProGleason-GAN在生成真实的合成前列腺组织图像方面取得了很好的结果.
- 该模型有效地合成了各种格里森等级,使得有针对性的数据增强成为可能.
- 这一框架为通过增强数据增强来改进人工智能驱动的前列腺癌诊断提供了可行的解决方案.
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