A New Graph Node Classification Benchmark: Learning Structure from Histology Cell Graphs
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.
Code (1)
Tasks
Graph LearningGraph Neural NetworkNode Classificationwhole slide imagesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images
Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do this, it is important to consider the overall tissue micro-environment by assessing the c…
Learning Whole-Slide Segmentation from Inexact and Incomplete Labels using Tissue Graphs
Segmenting histology images into diagnostically relevant regions is imperative to support timely and reliable decisions by pathologists. To this end, computer-aided techniques have been proposed to delineate relevant reg…
Node ClassificationSegmentationSemantic SegmentationWeakly supervised segmentation+22D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks
Cytoarchitecture describes the spatial organization of neuronal cells in the brain, including their arrangement into layers and columns with respect to cell density, orientation, or presence of certain cell types. It all…
DescriptiveGeneral ClassificationNode ClassificationCells are Actors: Social Network Analysis with Classical ML for SOTA Histology Image Classification
Digitization of histology images and the advent of new computational methods, like deep learning, have helped the automatic grading of colorectal adenocarcinoma cancer (CRA). Present automated CRA grading methods, howeve…
Deep Learningimage-classificationImage ClassificationDeep Multi-Resolution Dictionary Learning for Histopathology Image Analysis
The problem of recognizing various types of tissues present in multi-gigapixel histology images is an important fundamental pre-requisite for downstream analysis of the tumor microenvironment in a bottom-up analysis para…
Dictionary Learning