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

Ying Jia Lin, I. Fang Chung

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728146669
DOIs
StatePublished - Nov 2019
Event24th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2019 - Kaohsiung, Taiwan
Duration: 21 Nov 201923 Nov 2019

Publication series

NameProceedings - 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
Country/TerritoryTaiwan
CityKaohsiung
Period21/11/1923/11/19

Keywords

  • AC-GAN
  • GAN
  • hip fractures
  • image generation

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