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Context-aware convolutional neural network for grading of colorectal cancer histology images
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Shaban, Muhammad, Awan, Ruqayya, Fraz, Muhammad Moazam, Azam, Ayesha, Tsang, Yee-Wah, Snead, David and Rajpoot, Nasir M. (Nasir Mahmood) (2020) Context-aware convolutional neural network for grading of colorectal cancer histology images. IEEE Transactions on Medical Imaging, 39 (7). pp. 2395-2405. doi:10.1109/TMI.2020.2971006 ISSN 0278-0062.
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WRAP-context-aware-convolutional-neural-grading-cancer-images-Rajpootf-2020.pdf - Accepted Version - Requires a PDF viewer. Download (9Mb) | Preview |
Official URL: http://dx.doi.org/10.1109/TMI.2020.2971006
Abstract
Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. 224 × 224) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate a larger context by a context-aware neural network based on images with a dimension of 1792 × 1792 pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. We evaluated the proposed method on two colorectal cancer datasets for the task of cancer grading. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods. We also presented a comprehensive analysis of different variants of the proposed method.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Image processing -- Digital techniques, Machine learning, Tumors -- Classification, Pathology -- Data processing, Pathology—Slides (Photography), Histology, Pathological -- Computer programs | ||||||||||||
| Journal or Publication Title: | IEEE Transactions on Medical Imaging | ||||||||||||
| Publisher: | IEEE | ||||||||||||
| ISSN: | 0278-0062 | ||||||||||||
| Official Date: | July 2020 | ||||||||||||
| Dates: |
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| Volume: | 39 | ||||||||||||
| Number: | 7 | ||||||||||||
| Page Range: | pp. 2395-2405 | ||||||||||||
| DOI: | 10.1109/TMI.2020.2971006 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| Date of first compliant deposit: | 3 March 2020 | ||||||||||||
| Date of first compliant Open Access: | 5 March 2020 | ||||||||||||
| RIOXX Funder/Project Grant: |
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