论文标题
一个新的图节点分类基准：从组织学细胞图中学习结构
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs
论文作者
论文摘要
我们引入了一个新的基准数据集胎盘，以在一个未置换的域中进行淋巴结分类：从胎盘组织学中的细胞图中预测微解剖组织结构整个幻灯片图像。由于几个原因，这个问题对于图形学习是唯一的挑战。 Cell graphs are large (>1 million nodes per image), node features are varied (64-dimensions of 11 types of cells), class labels are imbalanced (9 classes ranging from 0.21% of the data to 40.0%), and cellular communities cluster into heterogeneously distributed tissues of widely varying sizes (from 11 nodes to 44,671 nodes for a single structure).在这里，我们发布了一个数据集，该数据集由两个胎盘组织学图像的两个细胞图组成，总计2,395,747个节点，其中799,745个具有地面真相标签。我们为7种可伸缩模型提供了电感基准结果，并展示了细胞图的独特品质如何帮助推动新型图神经网络体系结构的开发。
We introduce a new benchmark dataset, Placenta, for node classification in an underexplored domain: predicting microanatomical tissue structures from cell graphs in placenta histology whole slide images. This problem is uniquely challenging for graph learning for a few reasons. Cell graphs are large (>1 million nodes per image), node features are varied (64-dimensions of 11 types of cells), class labels are imbalanced (9 classes ranging from 0.21% of the data to 40.0%), and cellular communities cluster into heterogeneously distributed tissues of widely varying sizes (from 11 nodes to 44,671 nodes for a single structure). Here, we release a dataset consisting of two cell graphs from two placenta histology images totalling 2,395,747 nodes, 799,745 of which have ground truth labels. We present inductive benchmark results for 7 scalable models and show how the unique qualities of cell graphs can help drive the development of novel graph neural network architectures.
