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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] [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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Mortezapouraghdam Z, Haab L, Corona-Strauss FI, Steidl G, Strauss DJ. Assessment of Long-Term Habituation Correlates in Event-Related Potentials Using a von Mises Model. IEEE Trans Neural Syst Rehabil Eng 2015; 23:363-73. [DOI: 10.1109/tnsre.2014.2361614] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Ding X, Li Y, Belatreche A, Maguire LP. Novelty detection using level set methods. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2015; 26:576-588. [PMID: 25720011 DOI: 10.1109/tnnls.2014.2320293] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This paper presents a level set boundary description (LSBD) approach for novelty detection that treats the nonlinear boundary directly in the input space. The proposed approach consists of level set function (LSF) construction, boundary evolution, and termination of the training process. It employs kernel density estimation to construct the LSF of the initial boundary for the training data set. Then, a sign of the LSF-based algorithm is proposed to evolve the boundary and make it fit more tightly in the data distribution. The training process terminates when an expected fraction of rejected normal data is reached. The evolution process utilizes the signs of the LSF values at all training data points to decide whether to expand or shrink the boundary. Extensive experiments are conducted on benchmark data sets to evaluate the proposed LSBD method and compare it against four representative novelty detection methods. The experimental results demonstrate that the novelty detector modeled with the proposed LSBD can effectively detect anomalies.
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Ding X, Li Y, Belatreche A, Maguire LP. An experimental evaluation of novelty detection methods. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.12.002] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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