Medical Data Augmentation Using Generative Adversarial Networks: X-ray Image Generation for Transfer Learning of Hip Fracture Detection

Ying Jia Lin, I. Fang Chung

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

In the medical domain, it is difficult to retrieve large datasets. Classic data augmentation methods such as flipping, rotating, and scaling, are helpful in classification tasks, but these methods usually are not good enough to increase diversities and variances in small datasets. In this study, we applied AC-GAN (Auxiliary Classifier GANs) for data augmentation of our Limb X-ray dataset. We pre-trained the model for hip fracture detection of our Pelvic X-ray dataset by utilizing transfer learning with the augmented Limb X-ray dataset, which contained both the original dataset and realistic synthetic images made by the AC-GAN. The final hip fracture classification results of the Pelvic X-ray dataset showed that our generative model not only succeeded in producing realistic Limb X-ray data but also helped improve the performance of the transfer learning model for hip fracture detection.

原文English
主出版物標題Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728146669
DOIs
出版狀態Published - 11月 2019
事件24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 - Kaohsiung, Taiwan
持續時間: 21 11月 201923 11月 2019

出版系列

名字Proceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019

Conference

Conference24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
國家/地區Taiwan
城市Kaohsiung
期間21/11/1923/11/19

指紋

深入研究「Medical Data Augmentation Using Generative Adversarial Networks: X-ray Image Generation for Transfer Learning of Hip Fracture Detection」主題。共同形成了獨特的指紋。

引用此