Abdar, M., Samami, M., Dehghani Mahmoodabad, S., Doan, T., Mazoure, B., Hashemifesharaki, R., Liu, L., Khosravi, A., Acharya, U. R., Makarenkov, V., & Nahavandi, S. (2021). Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning. Computers in Biology and Medicine, 135, 104418. https://doi.org/10.1016/j.compbiomed.2021.104418
Uncertainty quantification in skin cancer classification using three-way decision-based Bayesian deep learning
|Author:||Abdar, Moloud1; Samami, Maryam2; Mahmoodabad, Sajjad Dehghani3;|
1Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, Australia
2Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
3Department of Artificial Intelligence, Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
4Department of Computer Science, McGill University / Mila, Montreal, Canada
5Department of Research and Development, Mute Hammer LLC., Santa Monica, USA
6Center for Machine Vision and Signal Analysis (CMVS), University of Oulu, Finland
7School of Engineering, Ngee Ann Polytechnic, Singapore
8Department of Biomedical Engineering, Singapore University of Social Sciences, Singapore
9Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
10Department of Computer Science, University of Quebec in Montreal, Montreal, Canada
|Online Access:||PDF Full Text (PDF, 2.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022030121347
|Publish Date:|| 2022-04-28
Accurate automated medical image recognition, including classification and segmentation, is one of the most challenging tasks in medical image analysis. Recently, deep learning methods have achieved remarkable success in medical image classification and segmentation, clearly becoming the state-of-the-art methods. However, most of these methods are unable to provide uncertainty quantification (UQ) for their output, often being overconfident, which can lead to disastrous consequences. Bayesian Deep Learning (BDL) methods can be used to quantify uncertainty of traditional deep learning methods, and thus address this issue. We apply three uncertainty quantification methods to deal with uncertainty during skin cancer image classification. They are as follows: Monte Carlo (MC) dropout, Ensemble MC (EMC) dropout and Deep Ensemble (DE). To further resolve the remaining uncertainty after applying the MC, EMC and DE methods, we describe a novel hybrid dynamic BDL model, taking into account uncertainty, based on the Three-Way Decision (TWD) theory. The proposed dynamic model enables us to use different UQ methods and different deep neural networks in distinct classification phases. So, the elements of each phase can be adjusted according to the dataset under consideration. In this study, two best UQ methods (i.e., DE and EMC) are applied in two classification phases (the first and second phases) to analyze two well-known skin cancer datasets, preventing one from making overconfident decisions when it comes to diagnosing the disease. The accuracy and the F1-score of our final solution are, respectively, 88.95% and 89.00% for the first dataset, and 90.96% and 91.00% for the second dataset. Our results suggest that the proposed TWDBDL model can be used effectively at different stages of medical image analysis.
Computers in biology and medicine
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This research was partially supported by the Australian Research Council's Discovery Projects funding scheme (project DP190102181).
© 2021 Elsevier Ltd. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.