GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
| Autor: | Maayan Frid-Adar, Michal Amitai, Jacob Goldberger, Hayit Greenspan, Idit Diamant, Eyal Klang |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Vision and Pattern Recognition (cs.CV) Cognitive Neuroscience Computer Science - Computer Vision and Pattern Recognition Machine Learning (stat.ML) 02 engineering and technology Convolutional neural network Machine Learning (cs.LG) 030218 nuclear medicine & medical imaging Image (mathematics) 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Contextual image classification business.industry Deep learning Pattern recognition Computer Science Applications Visualization Liver lesion 020201 artificial intelligence & image processing Artificial intelligence business |
| Popis: | Deep learning methods, and in particular convolutional neural networks (CNNs), have led to an enormous breakthrough in a wide range of computer vision tasks, primarily by using large-scale annotated datasets. However, obtaining such datasets in the medical domain remains a challenge. In this paper, we present methods for generating synthetic medical images using recently presented deep learning Generative Adversarial Networks (GANs). Furthermore, we show that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification. Our novel method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). We first exploit GAN architectures for synthesizing high quality liver lesion ROIs. Then we present a novel scheme for liver lesion classification using CNN. Finally, we train the CNN using classic data augmentation and our synthetic data augmentation and compare performance. In addition, we explore the quality of our synthesized examples using visualization and expert assessment. The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results increased to 85.7% sensitivity and 92.4% specificity. We believe that this approach to synthetic data augmentation can generalize to other medical classification applications and thus support radiologists' efforts to improve diagnosis. Preprint submitted to Neurocomputing |
| Databáze: | OpenAIRE |
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