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Cells are actors : social network analysis with classical ML for SOTA histology image classification
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Zamanitajeddin, Neda, Jahanifar, Mostafa and Rajpoot, Nasir M. (Nasir Mahmood) (2021) Cells are actors : social network analysis with classical ML for SOTA histology image classification. In: de Bruijne, Marleen and Cattin, Philippe C. and Cotin, Stéphane and Padoy, Nicolas and Speidel, Stefanie and Zheng, Yefeng and Essert, Caroline, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. Lecture Notes in Computer Science, 12908 . Cham: Springer International Publishing, pp. 288-298. ISBN 9783030872366
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Official URL: http://dx.doi.org/10.1007/978-3-030-87237-3_28
Abstract
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, however, usually use tiny image patches and thus fail to integrate the entire tissue micro-architecture for grading purposes. To tackle these challenges, we propose to use a statistical network analysis method to describe the complex structure of the tissue micro-environment by modelling nuclei and their connections as a network. We show that by analyzing only the interactions between the cells in a network, we can extract highly discriminative statistical features for CRA grading. Unlike other deep learning or convolutional graph-based approaches, our method is highly scalable (can be used for cell networks consist of millions of nodes), completely explainable, and computationally inexpensive. We create cell networks on a broad CRC histology image dataset, experiment with our method, and report state-of-the-art performance for the prediction of three-class CRA grading.
| Item Type: | Book Item | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Science > Computer Science | ||||
| Series Name: | Lecture Notes in Computer Science | ||||
| Publisher: | Springer International Publishing | ||||
| Place of Publication: | Cham | ||||
| ISBN: | 9783030872366 | ||||
| ISSN: | 0302-9743 | ||||
| Book Title: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 | ||||
| Editor: | de Bruijne, Marleen and Cattin, Philippe C. and Cotin, Stéphane and Padoy, Nicolas and Speidel, Stefanie and Zheng, Yefeng and Essert, Caroline | ||||
| Official Date: | 2021 | ||||
| Dates: |
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| Volume: | 12908 | ||||
| Page Range: | pp. 288-298 | ||||
| DOI: | 10.1007/978-3-030-87237-3_28 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published |
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