Efficient Colon Cancer Grading with Graph Neural Networks (2010.01091v1)

Abstract: 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.