TL;DR本文介绍了一种基于图神经网络和DenseNet的方法,使用颜色选择和生成的样本patches并保持图像信息的关联性,以对肺癌的亚型进行精确分类。模型在The Cancer Genome Atlas数据集上表现出了88.8%的准确率和0.89的AUC。
Abstract
representation learning for Whole Slide Images (WSIs) is pivotal in developing image-based systems to achieve higher precision in diagnostic pathology. We propose a two-stage framework for WSI representation learning