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GCN Based Deep Learning for Effective Classification of Breast Cancer Estrogen Receptor Status Using a Single Image from TMA
  • Atefeh Azin Kousha,
  • Jingxin Zhang
Atefeh Azin Kousha

Corresponding Author:[email protected]

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Jingxin Zhang


This paper presents a novel Graph-Convolutional-Network Based Deep-Learning (GCDL) approach to effectively determine estrogen-receptor-status (ERS) in patients with invasive breast cancer, from their Hematoxylin-and-Eosin (H&E) stained Tissue Microarray (TMA) images. Exploring previously overlooked correlation between ERS and nuclei spatial properties from H&E images, we use a set of novel methods to 1) construct twolevel cell-graphs from breast cancer H&E-stained tissue images and a novel graph node pooling, 2) extract two-level graph-metrics, 3) find the feature distribution statistics on low-level graph, 4) combine the features from 2) and 3) with conventional nuclei morphometric features to form composite node embeddings on high-level subgraph, and 5) associate a single-layer graph convolution with a stack of several nonlinear dense layers to create a GCDL classifier for enhanced performance. The proposed GCDL approach is tested and compared with a popular residual based Convolutional-Neural-Network (ResNet) method on a dataset of 960 patients with their ERSs diagnosed by pathologists. The test results show that the GCDL approach outperforms ResNet, achieving the best accuracy by far in ERS classification using a single H&E-stained TMA image per patient. Moreover, the GCDL approach requires much less training data and training time to achieve competitive results.
16 May 2024Submitted to TechRxiv
21 May 2024Published in TechRxiv