Name
Title | ||
|---|---|---|
CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images |
Abstract | ||
|---|---|---|
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 cell-level information along with the morphology of the gland. However, current automated methods for CRC grading typically utilise small image patches and therefore fail to incorporate the entire tissue micro-architecture for grading purposes. To overcome the challenges of CRC grading, we present a novel cell-graph convolutional neural network (CGC-Net) that converts each large histology image into a graph, where each node is represented by a nucleus within the original image and cellular interactions are denoted as edges between these nodes according to node similarity. The CGC-Net utilises nuclear appearance features in addition to the spatial location of nodes to further boost the performance of the algorithm. To enable nodes to fuse multi-scale information, we introduce Adaptive GraphSage, which is a graph convolution technique that combines multi-level features in a data-driven way. Furthermore, to deal with redundancy in the graph, we propose a sampling technique that removes nodes in areas of dense nuclear activity. We show that modeling the image as a graph enables us to effectively consider a much larger image (around 16× larger) than traditional patch-based approaches and model the complex structure of the tissue micro-environment. We construct cell graphs with an average of over 3,000 nodes on a large CRC histology image dataset and report state-of-the-art results as compared to recent patch-based as well as contextual patch-based techniques, demonstrating the effectiveness of our method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICCVW.2019.00050 | 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) |
Keywords | Field | DocType |
histology image analysis,cancer grading,colorectal cancer,graph convolutional network,deep learning | Graph,Computer vision,Grading (education),Pattern recognition,Computer science,Artificial intelligence,Colorectal cancer,Histology | Conference |
Volume | Issue | ISSN |
2019 | 1 | 2473-9936 |
ISBN | Citations | PageRank |
978-1-7281-5024-6 | 3 | 0.43 |
References | Authors | |
14 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanning Zhou | 1 | 14 | 1.71 |
| Simon J Graham | 2 | 182 | 29.44 |
| Navid Alemi Koohbanani | 3 | 22 | 4.35 |
| Muhammad Shaban | 4 | 3 | 0.43 |
| Pheng-Ann Heng | 5 | 3565 | 280.98 |
| Nasir M Rajpoot | 6 | 6 | 3.51 |