MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification
Article
Senousy, Zakaria, Abdelsamea, Mohammed M., Gaber, Mohamed Medhat, Abdar, Moloud, Acharya, U. Rajendra, Khosravi, Abbas and Nahavandi, Saeid. 2022. "MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification." IEEE Transactions on Biomedical Engineering. 69 (2), pp. 818-829. https://doi.org/10.1109/TBME.2021.3107446
Article Title | MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification |
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ERA Journal ID | 5043 |
Article Category | Article |
Authors | Senousy, Zakaria, Abdelsamea, Mohammed M., Gaber, Mohamed Medhat, Abdar, Moloud, Acharya, U. Rajendra, Khosravi, Abbas and Nahavandi, Saeid |
Journal Title | IEEE Transactions on Biomedical Engineering |
Journal Citation | 69 (2), pp. 818-829 |
Number of Pages | 12 |
Year | 2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 0018-9294 |
1558-2531 | |
Digital Object Identifier (DOI) | https://doi.org/10.1109/TBME.2021.3107446 |
Web Address (URL) | https://ieeexplore.ieee.org/document/9525263 |
Abstract | Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multi-level Context and Uncertainty aware ( MCUa ) dynamic deep learning ensemble model. MCUa model consists of several multi-level context-aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUa model has achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models. |
Keywords | Breast cancer; convolutional neural networks; context-awareness; histology images; uncertainty quantification |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Birmingham City University, United Kingdom |
Assiut University, Egypt | |
Deakin University | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
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https://research.usq.edu.au/item/z1w06/mcua-multi-level-context-and-uncertainty-aware-dynamic-deep-ensemble-for-breast-cancer-histology-image-classification
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