论文标题
图形神经网络有效的结肠癌分级
Efficient Colon Cancer Grading with Graph Neural Networks
论文作者
论文摘要
在处理分级结直肠癌图像的应用时，这项工作提出了从组织病理学图像预测癌症水平的三步管道。与结直肠癌分级数据集的其他最先进的方法相比，总体模型的性能更好，并且在扩展的结直肠癌分级集方面表现出色。性能改进可以归因于两个主要因素：此处描述的特征选择和图形增强方法在空间上意识到，但总体像素位置独立。此外，就模型的预测和足够大型模型的准确性而言，节点方面的图大小变得稳定。图神经网络本身由三个卷积块和线性层组成，与此应用程序的其他网络相比，这是一个相当简单的设计。
Dealing with the application of grading colorectal cancer images, this work proposes a 3 step pipeline for prediction of cancer levels from a histopathology image. The overall model performs better compared to other state of the art methods on the colorectal cancer grading data set and shows excellent performance for the extended colorectal cancer grading set. The performance improvements can be attributed to two main factors: The feature selection and graph augmentation method described here are spatially aware, but overall pixel position independent. Further, the graph size in terms of nodes becomes stable with respect to the model's prediction and accuracy for sufficiently large models. The graph neural network itself consists of three convolutional blocks and linear layers, which is a rather simple design compared to other networks for this application.
