Handling imbalanced medical image data: A deep-learning-based one-class classification approach
[Display omitted] •A novel deep-learning-based model for the data imbalance problem.•Effective perturbing operations to capture single-class-relevant features.•State-of-the-art performance on four imbalanced medical image datasets. In clinical settings, a lot of medical image datasets suffer from th...
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| Vydáno v: | Artificial intelligence in medicine Ročník 108; s. 101935 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Netherlands
Elsevier B.V
01.08.2020
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| Témata: | |
| ISSN: | 0933-3657, 1873-2860, 1873-2860 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | [Display omitted]
•A novel deep-learning-based model for the data imbalance problem.•Effective perturbing operations to capture single-class-relevant features.•State-of-the-art performance on four imbalanced medical image datasets.
In clinical settings, a lot of medical image datasets suffer from the imbalance problem which hampers the detection of outliers (rare health care events), as most classification methods assume an equal occurrence of classes. In this way, identifying outliers in imbalanced datasets has become a crucial issue. To help address this challenge, one-class classification, which focuses on learning a model using samples from only a single given class, has attracted increasing attention. Previous one-class modeling usually uses feature mapping or feature fitting to enforce the feature learning process. However, these methods are limited for medical images which usually have complex features. In this paper, a novel method is proposed to enable deep learning models to optimally learn single-class-relevant inherent imaging features by leveraging the concept of imaging complexity. We investigate and compare the effects of simple but effective perturbing operations applied to images to capture imaging complexity and to enhance feature learning. Extensive experiments are performed on four clinical datasets to show that the proposed method outperforms four state-of-the-art methods. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0933-3657 1873-2860 1873-2860 |
| DOI: | 10.1016/j.artmed.2020.101935 |