Medical Imaging Technology
Online ISSN : 2185-3193
Print ISSN : 0288-450X
ISSN-L : 0288-450X
特集/医用画像処理におけるGenerative Adversarial Networksの利用
Learning More with Less: GAN-based Medical Image Augmentation
Changhee HANKohei MURAOShin’ichi SATOHHideki NAKAYAMA
ジャーナル 認証あり

2019 年 37 巻 3 号 p. 137-142


Convolutional Neural Network (CNN)-based accurate prediction typically requires large-scale annotated training data. In Medical Imaging, however, both obtaining medical data and annotating them by expert physicians are challenging; to overcome this lack of data, Data Augmentation (DA) using Generative Adversarial Networks (GANs) is essential, since they can synthesize additional annotated training data to handle small and fragmented medical images from various scanners―those generated images, realistic but completely novel, can further fill the real image distribution uncovered by the original dataset. As a tutorial, this paper introduces GAN-based Medical Image Augmentation, along with tricks to boost classification/object detection/segmentation performance using them, based on our experience and related work. Moreover, we show our first GAN-based DA work using automatic bounding box annotation, for robust CNN-based brain metastases detection on 256×256 MR images; GAN-based DA can boost 10% sensitivity in diagnosis with a clinically acceptable number of additional False Positives, even with highly-rough and inconsistent bounding boxes.

© 2019 The Japanese Society of Medical Imaging Technology
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