计算病理学的内容生成模型:对方法,应用和挑战的全面调查
IEEE reviews in biomedical engineering
|October 28, 2025
概括
计算病理学的内容生成模型有助于学习和数据增强. 本综述综合了图像,文本和分子数据生成方面的进展,强调了临床应用的未来方向.
科学领域:
- 计算病理学计算病理学
- 人工智能在医学中的应用
背景情况:
- 内容生成建模是计算病理学的快速发展领域.
- 它为数据高效学习,合成数据增强和以任务为导向的生成提供了巨大的潜力.
研究的目的:
- 为计算病理学的内容生成提供了近期进展的全面审查.
- 综合关键的发展,数据集,评估协议和现场限制.
主要方法:
- 系统分析了150多项用于计算病理学的内容生成的代表性研究.
- 研究对图像生成,文本生成,分子形状-形态生成和专业应用的分类.
主要成果:
- 从生成对抗网络 (GAN) 到扩散模型和视觉语言模型的架构的演变.
- 识别共同的数据集和评估指标.
- 突出限制,如高保真度全幻灯片图像生成和临床解释性.
结论:
- 内容生成对计算病理学具有前景,但在忠实性,可解释性和伦理考虑方面面临挑战.
- 未来的研究应该专注于集成的,临床部署的生成系统.
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