Pang, Ting and Wong, Jeannie Hsiu Ding and Ng, Wei Lin and Chan, Chee Seng (2021) Semi-supervise d GAN-base d radiomics model for data augmentation in breast ultrasound mass classification. Computer Methods and Programs in Biomedicine, 203. ISSN 0169-2607, DOI https://doi.org/10.1016/j.cmpb.2021.106018.
Full text not available from this repository.Abstract
Background and Objective: The capability of deep learning radiomics (DLR) to extract high-level medical imaging features has promoted the use of computer-aided diagnosis of breast mass detected on ultrasound. Recently, generative adversarial network (GAN) has aided in tackling a general issue in DLR, i.e., obtaining a sufficient number of medical images. However, GAN methods require a pair of input and labeled images, which require an exhaustive human annotation process that is very time-consuming. The aim of this paper is to develop a radiomics model based on a semi-supervised GAN method to perform data augmentation in breast ultrasound images. Methods: A total of 1447 ultrasound images, including 767 benign masses and 680 malignant masses were acquired from a tertiary hospital. A semi-supervised GAN model was developed to augment the breast ultrasound images. The synthesized images were subsequently used to classify breast masses using a convolutional neural network (CNN). The model was validated using a 5-fold cross-validation method. Results: The proposed GAN architecture generated high-quality breast ultrasound images, verified by two experienced radiologists. The improved performance of semi-supervised learning increased the quality of the synthetic data produced in comparison to the baseline method. We achieved more accurate breast mass classification results (accuracy 90.41%, sensitivity 87.94%, specificity 85.86%) with our synthetic data augmentation compared to other state-of-the-art methods. Conclusion: The proposed radiomics model has demonstrated a promising potential to synthesize and classify breast masses on ultrasound in a semi-supervised manner. (c) 2021 Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Funders: | Malaysian Ministry of Higher Education (MOHE) Fundamental Research Grant Scheme (FRGS) (FP017-2019A), Malaysian Ministry of Higher Education (MOHE) Fundamental Research Grant Scheme (FRGS) (FRGS/1/2019/SKK03/UM/01/1), University of Malaya Medical Centre (UMMC) Medical Ethics Committee (2019822-7771) |
| Uncontrolled Keywords: | Deep learning radiomics; Semi-supervised learning; Generative adversarial network; Data augmentation; Breast cancer classification; Ultrasound imaging |
| Subjects: | R Medicine > R Medicine (General) |
| Divisions: | Faculty of Medicine |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 31 Mar 2022 08:18 |
| Last Modified: | 31 Mar 2022 08:18 |
| URI: | http://eprints.um.edu.my/id/eprint/26625 |
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