CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images

Zhou, Yanning; Graham, Simon; Koohbanani, Navid Alemi; Shaban, Muhammad; Heng, Pheng‐Ann; Rajpoot, Nasir M.

doi:10.1109/iccvw.2019.00050

Cited by 142 publications

(113 citation statements)

References 41 publications

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“…To make a final prediction, this model transforms the local representation of a histology image into high-dimensional features, then combines the features by perceiving their spatial arrangement. Zhou et al [ 25 ] introduced a new cell-graph convolutional neural network (CGC-Net) for grading of colorectal cancer, which transforms each large histology image into a graph, with each node represented by a nucleus within the input image and cellular associations denoted as edges among these nodes based on node similarity.…”

Section: Related Workmentioning

confidence: 99%

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Senousy

Abdelsamea

Mohamed

et al. 2021

Entropy

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Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.

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Section: Related Workmentioning

confidence: 99%

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Senousy

Abdelsamea

Mohamed

et al. 2021

Entropy

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show abstract

“…The proposed method obtained an overall accuracy of 95.3% and the external validation returned an accuracy of 81.7%. Meanwhile, Zhou et al 62 proposed a pipeline to classify colorectal adenocarcinomas, based on the recent graph neural networks, converting each histopathological image into a graph, with nucleus and cellular interactions being represented by nodes and edges, respectively. The authors also propose a new graph convolution module, called Adaptive GraphSage, to combine multilevel features.…”

Section: Computational Pathology On Colorectal Cancermentioning

confidence: 99%

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

Oliveira

Neto

Fraga³

et al. 2021

Sci Rep

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Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

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“…The use of graphs as a means to derive insights in the biomedical domain is not new, in fact, there are several successful attempts at leveraging the technology. In [20] colo-rectal cancer grading is carried out by transforming histology images into a graph, in which cell nuclei are represented as nodes in the graphs and links made based on node similarity. Training on these with a GCN they are able to reach state of the art grading accuracy.…”

Section: Related Workmentioning

confidence: 99%

An Unsupervised Graph Embeddings Approach to Multiplex Immunofluorescence Image Exploration

Innocenti

Zhang

Selvaraj

et al. 2021

Preprint

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Understanding the complex biology of the tumor microenvironment (TME) is necessary to understand the mechanisms of action of immuno-oncology therapies and to match the right therapies to the right patients. Multiplex immunofluorescence (mIF) is a useful technology that has tremendous potential to further our understanding of cancer patho-biology; however, tools that fully leverage the high dimensionality of this data are still in their infancy. We describe here a novel deep learning pipeline aimed to allow Graph-based Inspection of Tissues via Embeddings, GraphITE. GraphITE transforms mIF data into a graph representation, where unsupervised learning algorithms can be utilised to generate embeddings representing cellular `neighbourhoods'. The embeddings can be downprojected and explored for clustering analysis, and patterns can be mapped back to the image as well as interrogated for phenotypical, morphological, or structural distinctiveness. GraphITE supports the extraction of information not only on the phenotypes of individual cells or the relationships between specific cell types, but is able to characterize cell neighborhoods to look for more complex interactions, thereby allowing pathologists and data scientists to explore mIF data sets, uncovering patterns that are otherwise obscured by the high-dimensionality of the data. In this work, we showcase the current setup of the system, going from raw input data all the way to a user friendly exploration tool. Using this tool, we show how the data can be navigated in a way previously not possible.

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CGC-Net: Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images

Abstract: Figure 1. A histology image (a) is typically broken into small image patches (b) for cancer grading. We propose to utilise the cell graph (d) that is built from individual nuclei after segmentation (c) to model the entire tissue micro-environment for cancer grading.

Cited by 142 publications

References 41 publications

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

An Unsupervised Graph Embeddings Approach to Multiplex Immunofluorescence Image Exploration

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