通过图形神经网络进行基于集群的空间分析 (CluSA) 框架,用于使用基因病理图像进行慢性病预测
Joonsang Lee1, Elisa Warner2, Salma Shaikhouni3
1Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. azjslee@gmail.com.
Scientific reports
|August 5, 2023
概括
一种新的机器学习方法,基于集群的空间分析 (CluSA),使用无监督学习来分析脏组织图像以预测慢性脏病 (CKD),而无需专家注释.
科学领域:
- 数字病理学数字病理学
- 计算病理学计算病理学
- 机器学习在医学中的应用
背景情况:
- 数字病理学中的机器学习有助于评估功能和诊断慢性病 (CKD).
- 当前的方法往往需要广泛的专家注释,这耗时且不切实际.
- 需要自动化,高效的方法来分析病理图像,以评估病.
研究的目的:
- 开发和验证一个新的计算框架,基于集群的空间分析 (CluSA),用于功能评估和CKD诊断.
- 利用无监督学习来识别脏组织中的空间关系,尽量减少对专家注释的需求.
- 使用CluSA预测预计的淋巴细胞过率 (eGFR) 在活检和eGFR变化超过一年.
主要方法:
- 开发了CluSA,一种无监督的机器学习框架,用于学习组织中局部视觉模式之间的空间关系.
- 利用了来自172个活检核的107,471张基因病理图像用于集群和深度学习模型训练.
- 通过颜色编码集群模式并通过图形神经网络进行空间分析来集成空间信息.
- 使用随机森林分类器预测CKD和eGFR使用各种特征组.
主要成果:
- 在活检时预测eGFR的高准确度:0.95准确度,0.97灵敏度,0.90特异性和0.96AUC.
- 在预测一年的eGFR变化方面表现强:准确度为0.84,灵敏度为0.83,特异性为0.85,AUC为0.85.
- 在没有专家注释的情况下,CluSA框架成功地确定了功能和预后的新型预测因子.
结论:
- 克鲁萨代表了第一个基于无监督机器学习的空间分析框架,用于病理.
- 该框架准确地分类和预测功能和预后,而不需要专家注释.
- 通过识别新的预测特征,CluSA提供了一种强大的工具,用于推进病研究和临床诊断.
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