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图形神经网络在组织病理学:新兴趋势和未来的方向
Siemen Brussee1, Giorgio Buzzanca1, Anne M R Schrader1
1Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
Medical image analysis
|January 10, 2025
概括
与卷积神经网络 (CNN) 相比,图形神经网络 (GNN) 在组织病理学中提供了更好的空间理解. 本综述探讨了GNN应用程序和先进的全幻灯片图像分析的新兴趋势.
科学领域:
- 数字病理学数字病理学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 整张幻灯片图像 (WSI) 的组织病理学分析越来越多地使用深度学习,特别是卷积神经网络 (CNN).
- 美国有线电视新闻网 (CNN) 努力在世界互联网中捕捉复杂的空间关系.
- 图形神经网络 (GNN) 擅长模拟WSIs中的对互动和拓结构.
研究的目的:
- 为了提供GNN在组织病理学中的全面审查.
- 讨论当前的应用,并确定WSI分析GNN的新兴趋势.
- 建议未来的研究方向,以利用GNN推进基因病理学分析.
主要方法:
- 文献综述专注于GNN在组织病理学中的应用.
- 在WSI分析中对GNN新兴趋势的定量分析.
- 识别和讨论四个主要趋势:等级GNN,自适应图形结构学习,多模式GNN和高阶GNN.
主要成果:
- 在WSIs中捕获拓和空间信息方面,GNN是有效的,克服了CNN的局限性.
- 鉴定和分析了四个重要的新兴趋势在GNN用于组织病理学.
- 该审查提供了对数字病理学中GNN不断变化的景观的见解.
结论:
- GNN代表了一种强大的工具,可以增强WSI的遗传病理学分析.
- 像等级,自适应,多模式和高阶GNN等新兴趋势显示出显著的前景.
- 对GNN的进一步研究将推动数字病理学和疾病诊断方面的创新.
关键词:
适应式图形结构学习学习计算病理学计算病理学图形神经网络是一个神经网络.图形表示学习学习学习图形表示.层次图表表示学习学习学习.高级图形表示学习学习学习的高阶图形表示.多模式图表表示学习学习学习多模式图表表示学习更多相关视频
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