以CLIP为指导的病理核心生成网络图像增强图像增强
Yanan Zhang1, Qingyang Liu1, Qian Chen1
1Image Processing Center, Beihang University, Beijing, 102206, China.
Medical image analysis
|December 20, 2025
概括
本研究引入了一种使用CLIP引导的生成对抗网络 (GAN) 的新型数据增强方法,以改善计算病理学中的核细分和分类. 该方法通过生成各种合成病理图像而提高深度学习模型的性能,而无需手动注释.
科学领域:
- 计算病理学计算病理学
- 医疗图像分析 医学图像分析
- 深度学习是一种深度学习.
背景情况:
- 准确的核细分和分类对于计算病理学 (CPath) 是至关重要的.
- 高的注释成本限制了病理学图像的深度学习模型培训数据.
- 现有的生成对抗网络 (GAN) 在核面具的多类数据可扩展性方面扎.
研究的目的:
- 开发一种基于CLIP的生成数据增强方法,用于核的细分和分类.
- 克服当前GAN在生成多样化,多类病理学数据方面的局限性.
- 通过合成数据生成,提高深度学习模型在计算病理学的性能.
主要方法:
- 使用了CLIP引导的生成数据增强方法,使用了病态的CLIP文本和图像编码器.
- 从基因病理图像和细胞核面具 (器官类型,细胞计数,细胞核类型) 生成文本描述.
- 采用多模条件图像生成器和双分辨器 (高分辨率和基于CLIP) 来实现现实的图像合成.
主要成果:
- 在各种公共病理核数据集上证明了拟议方法的有效性.
- 在核细分和分类任务中实现了性能改进.
- 通过定性和定量分析验证了该方法,突出了与现有方法相比的优势.
结论:
- 以CLIP为指导的生成数据增强方法显著提高了计算病理学中的核细分和分类.
- 这种方法有效地扩展了培训数据集,而不需要额外的手动注释,解决了一个关键的瓶.
- 该方法在提高数字病理学深度学习的准确性和可扩展性方面具有前景.
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