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Gao L, Zhang L, Liu C, Wu S. Handling imbalanced medical image data: A deep-learning-based one-class classification approach. Artif Intell Med 2020; 108:101935. [PMID: 32972664 PMCID: PMC7519174 DOI: 10.1016/j.artmed.2020.101935] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 06/20/2020] [Accepted: 07/17/2020] [Indexed: 11/17/2022]
Abstract
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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Affiliation(s)
- Long Gao
- College of Computer, National University of Defense Technology, Changsha, 410073, China; Department of Radiology, School of Medicine, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA.
| | - Lei Zhang
- Department of Radiology, School of Medicine, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA
| | - Chang Liu
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA
| | - Shandong Wu
- Department of Radiology, School of Medicine, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA; Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA; Department of Biomedical Informatics, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA; Intelligent Systems Program, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA.
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