Context-aware convolutional neural network for grading of colorectal cancer histology images
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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.
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Histopathologic classification studies have been reported in colorectal cancer.
“Histopathologic classification studies have been reported in colorectal cancer (70,71).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [70]
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SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images
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Self-citationJoint categorical and ordinal learning for cancer grading in pathology images
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Enhancing generalizability of data-driven urban flood models by incorporating contextual information
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Self-citationBayesian Collaborative Learning for Whole-Slide Image Classification
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RetractedKnowledge Distillation in Histology Landscape by Multi-Layer Features Supervision
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Hybrid Supervision Learning for Pathology Whole Slide Image Classification
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Polyp Segmentation via Semantic Enhanced Perceptual Network
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A Scalable Graph-Based Framework for Multi-Organ Histology Image Classification
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A Federated Learning Approach to Tumor Detection in Colon Histology Images
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Self-citationA Multimodal Transfer Learning Approach Using PubMedCLIP for Medical Image Classification
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DONet: Dual Objective Networks for Skin Lesion SegmentationPreprint
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Deep Multi-Resolution Dictionary Learning for Histopathology Image AnalysisPreprint
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Multiscale Dilated UNet for Segmentation of Multi-Organ Nuclei in Digital Histology Images
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Knowledge-Enhanced Visual-Language Pretraining for Computational Pathology
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FedDropoutAvg: Generalizable federated learning for histopathology image classificationPreprint
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Self-citationKnowledge-enhanced pretraining for vision-language pathology foundation model on cancer diagnosis
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Is the U-NET Directional-Relationship Aware?
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CoC-ResNet - classification of colorectal cancer on histopathologic images using residual networks
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SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis
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- Authors
- Shaban, M., Awan, R., Fraz, M. M., Azam, A., Tsang, Y.-W., Snead, D., & Rajpoot, N. M.
- Affiliations
- University of Warwick (GB)
- University Hospital Coventry (GB)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-07
- Volume
- 39
- Issue
- 7
- Pages
- 2395-2405
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2964056701
- Cited by
- 179
- References
- 46
- Citations by year
- 2026: 11 · 2025: 21 · 2024: 32 · 2023: 39 · 2022: 32 · 2021: 35 · 2020: 8 · 2019: 1
- FWCI
- 11.20
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Medical Imaging (publishedVersion)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Engineering and Physical Sciences Research Council (1829583)
- The Alan Turing Institute through the EPSRC (EP/510129/1)
- Medical Research Council (MRC) award for the MICAHIL project (MR/P015476/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Keywords
- colorectal cancer, cancer grading, feature aggregation, spatial context, whole slide image classification
- MeSH terms
- Histological Techniques, Humans, Colorectal Neoplasms, Neural Networks, Computer
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