图表神经网络用于代表肺癌的学习
Rukhma Aftab1, Yan Qiang2, Juanjuan Zhao1
1College of Information and Computer, Taiyuan University of Technology, No. 79 Yingze West Street, Taiyuan, 030024, China.
BMC cancer
|October 26, 2023
概括
本研究介绍了MIL-GNN,这是一个基于图形的模型,用于在整个幻灯片图像上进行多个实例学习. 它有效地分类肺癌亚型,并提高诊断病理学精度.
科学领域:
- 计算病理学计算病理学
- 机器学习用于医学成像.
背景情况:
- 基于图像的诊断对于病理学至关重要.
- 多个实例学习 (MIL) 是整个幻灯片图像 (WSIs) 的关键.
- 目前的神经网络方法在MIL中显示出有前途.
研究的目的:
- 开发一个基于图形的模型,用于MIL的端到端学习,用于WSIs.
- 建议MIL-GNN用于发现补丁关系和聚合特征.
- 通过基于图像的系统来提高诊断病理学精度.
主要方法:
- 一个基于图形的变量自动编码器与高斯混合模型 (MIL-GNN).
- 一种基于的方法,用于采样合适的补丁.
- 终端到终端的学习框架.
主要成果:
- 在MUSK数据集上获得了97.42%的准确性.
- 在分类肺癌亚型 (LUAD和LUSC) 中达到94.3%的AUC.
- 拟议的MIL-GNN技术的证明有效性.
结论:
- MIL-GNN有效地将补丁信息汇总成一个单一的矢量表示.
- 基于图形的方法提高了WSIs的MIL性能.
- 这种方法显示了改善癌症亚型分类的巨大潜力.
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