Aleatory-aware deep uncertainty quantification for transfer learning
Article
Kabir, H M Dipu, Khanam, Sadia, Khozeimeh, Fahime, Khosravi, Abbas, Mondal, Subrota Kumar, Nahavandi, Saeid and Acharya, U. Rajendra. 2022. "Aleatory-aware deep uncertainty quantification for transfer learning." Computers in Biology and Medicine. 143. https://doi.org/10.1016/j.compbiomed.2022.105246
| Article Title | Aleatory-aware deep uncertainty quantification for transfer learning |
|---|---|
| ERA Journal ID | 5040 |
| Article Category | Article |
| Authors | Kabir, H M Dipu, Khanam, Sadia, Khozeimeh, Fahime, Khosravi, Abbas, Mondal, Subrota Kumar, Nahavandi, Saeid and Acharya, U. Rajendra |
| Journal Title | Computers in Biology and Medicine |
| Journal Citation | 143 |
| Article Number | 105246 |
| Number of Pages | 14 |
| Year | 2022 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0010-4825 |
| 1879-0534 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2022.105246 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0010482522000385 |
| Abstract | The user does not have any idea about the credibility of outcomes from deep neural networks (DNN) when uncertainty quantification (UQ) is not employed. However, current Deep UQ classification models capture mostly epistemic uncertainty. Therefore, this paper aims to propose an aleatory-aware Deep UQ method for classification problems. First, we train DNNs through transfer learning and collect numeric output posteriors for all training samples instead of logical outputs. Then we determine the probability of happening a certain class from K-nearest output posteriors of the same DNN in training samples. We name this probability as opacity score, as the paper focuses on the detection of opacity on X-ray images. This score reflects the level of aleatory on the sample. When the NN is certain on the classification of the sample, the probability of happening a class becomes much higher than the probabilities of others. Probabilities for different classes become close to each other for a highly uncertain classification outcome. To capture the epistemic uncertainty, we train multiple DNNs with different random initializations, model selection, and augmentations to observe the effect of these training parameters on prediction and uncertainty. To reduce execution time, we first obtain features from the pre-trained NN. Then we apply features to the ensemble of fully connected layers to get the distribution of opacity score during the test. We also train several ResNet and DenseNet DNNs to observe the effect of model selection on prediction and uncertainty. The paper also demonstrates a patient referral framework based on the proposed uncertainty quantification. The scripts of the proposed method are available at the following link: https://github.com/dipuk0506/Aleatory-aware-UQ. |
| Keywords | Aleatoric; Patient referral ; Uncertainty; COVID; Heteroscedastic; Epistemic |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Deakin University |
| Dhaka Dental College, Bangladesh | |
| Macau University of Science and Technology, China | |
| Harvard University, United States | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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https://research.usq.edu.au/item/z1vq8/aleatory-aware-deep-uncertainty-quantification-for-transfer-learning
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