Home > Published Issues > 2023 > Volume 11, No. 1, March 2023 >
JOIG 2023 Vol.11(1): 15-20
doi: 10.18178/joig.11.1.15-20

Cascaded Graph Convolution Approach for Nuclei Detection in Histopathology Images

Sachin S. Bahade*, Michael Edwards, and Xianghua Xie
Department of Computer Science, Swansea University, UK; Email: {michael.edwards, x.xie}@swansea.ac.uk
*Correspondence: s.s.bahade.885875@swansea.ac.uk (S.S.B.)

Manuscript received July 12, 2022; revised September 22, 2022; accepted October 23, 2022.

Abstract—Nuclei detection in histopathology images of cancerous tissue stained with conventional hematoxylin and eosin stain is a challenging task due to the complexity and diversity of cell data. Deep learning techniques have produced encouraging results in the field of nuclei detection, where the main emphasis is on classification and regressionbased methods. Recent research has demonstrated that regression-based techniques outperform classification. In this paper, we propose a classification model based on graph convolutions to classify nuclei, and similar models to detect nuclei using cascaded architecture. With nearly 29,000 annotated nuclei in a large dataset of cancer histology images, we evaluated the Convolutional Neural Network (CNN) and Graph Convolutional Networks (GCN) based approaches. Our findings demonstrate that graph convolutions perform better with a cascaded GCN architecture and are more stable than centre-of-pixel approach. We have compared our twofold evaluation quantitative results with CNN-based models such as Spatial Constrained Convolutional Neural Network (SC-CNN) and Centre-of-Pixel Convolutional Neural Network (CP-CNN). We used two different loss functions, binary cross-entropy and focal loss function, and also investigated the behaviour of CP-CNN and GCN models to observe the effectiveness of CNN and GCN operators. The compared quantitative F1 score of cascaded-GCN shows an improvement of 6% compared to state-of-the-art methods.

Keywords—Nuclei detection, graph convolution network, medical imaging

Cite: Sachin S. Bahade, Michael Edwards, and Xianghua Xie, "Cascaded Graph Convolution Approach for Nuclei Detection in Histopathology Images," Journal of Image and Graphics, Vol. 11, No. 1, pp. 15-20, March 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.