Guohua Shi1, Jiawen Wang1, Yan Qiang2, Xiaotang Yang3, Juanjuan Zhao1, Rui Hao1, Wenkai Yang1, Qianqian Du1, Ntikurako Guy-Fernand Kazihise1. 1. College of Information and Computer, Taiyuan University of Technology, Taiyuan, China. 2. College of Information and Computer, Taiyuan University of Technology, Taiyuan, China. Electronic address: qiangyan@tyut.edu.cn. 3. Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China. Electronic address: yxt1335123884@126.com.
Abstract
BACKGROUND AND OBJECTIVE: Image classification is an important task in many medical applications. Methods based on deep learning have made great achievements in the computer vision domain. However, they typically rely on large-scale datasets which are annotated. How to obtain such great datasets is still a serious problem in medical domain. METHODS: In this paper, we propose a knowledge-guided adversarial augmentation method for synthesizing medical images. First, we design Term and Image Encoders to extract domain knowledge from radiologists, then we use domain knowledge as novel condition to constrain the Auxiliary Classifier Generative Adversarial Network (ACGAN) framework for the synthesis of high-quality thyroid nodule images. Finally, we demonstrate our method on the task of classifying ultrasonography thyroid nodule. Our method can make effective use of the high-quality diagnostic experience of advanced radiologists. In addition, we creatively choose to extract domain knowledge from standardized terms rather than ultrasound images. RESULTS: Our novel method is demonstrated on a limited dataset of 1937 clinical thyroid ultrasound images and corresponding standardized terms. The accuracy of the proposed model for thyroid nodules is 91.46%, the sensitivity is 90.63%, the specificity is 92.65%, and the AUC is 95.32%, which is better than the current classification methods for thyroid nodules. The experimental results show the model has better generalization and robustness. CONCLUSIONS: We believe that the proposed method can alleviate the problem of insufficient data in the medical domain, and other medical problems can benefit from using synthetic augmentation.
BACKGROUND AND OBJECTIVE: Image classification is an important task in many medical applications. Methods based on deep learning have made great achievements in the computer vision domain. However, they typically rely on large-scale datasets which are annotated. How to obtain such great datasets is still a serious problem in medical domain. METHODS: In this paper, we propose a knowledge-guided adversarial augmentation method for synthesizing medical images. First, we design Term and Image Encoders to extract domain knowledge from radiologists, then we use domain knowledge as novel condition to constrain the Auxiliary Classifier Generative Adversarial Network (ACGAN) framework for the synthesis of high-quality thyroid nodule images. Finally, we demonstrate our method on the task of classifying ultrasonography thyroid nodule. Our method can make effective use of the high-quality diagnostic experience of advanced radiologists. In addition, we creatively choose to extract domain knowledge from standardized terms rather than ultrasound images. RESULTS: Our novel method is demonstrated on a limited dataset of 1937 clinical thyroid ultrasound images and corresponding standardized terms. The accuracy of the proposed model for thyroid nodules is 91.46%, the sensitivity is 90.63%, the specificity is 92.65%, and the AUC is 95.32%, which is better than the current classification methods for thyroid nodules. The experimental results show the model has better generalization and robustness. CONCLUSIONS: We believe that the proposed method can alleviate the problem of insufficient data in the medical domain, and other medical problems can benefit from using synthetic augmentation.
Authors: Eoin F Cleere; Matthew G Davey; Shane O'Neill; Mel Corbett; John P O'Donnell; Sean Hacking; Ivan J Keogh; Aoife J Lowery; Michael J Kerin Journal: Diagnostics (Basel) Date: 2022-03-24