Earticle

초록

영어

One of the main challenges of advancing medical imaging research is its data's privacy and sensitivity; sharing and distributing medical information is limited due to privacy concerns and the possible exploitation of personal information. Generative adversarial networks have impressive results in synthesizing new datasets from natural images and translating image to image. In the case of CycleGAN construct samples are done by translating the image from one domain to another. We present a study of the application of CycleGAN in medical imaging by converting standard images to images with a disease. Consequently, we test the generated dataset in a classification task and compare it with the original one. Results reveal that the synthesized samples could replace the original dataset

목차

Abstract
1. Introduction
2. Related Works
2.1. Vanilla Generative Adversarial Networks
2.2. CycleGAN
3. Methods
3.1. Dataset
3.2. Experiment Setup
4. Experiment Result
4.1. Classification Evaluation
5. Conclusions
Acknowledgment

저자

• Zineb Tissir [ Department of AI Software Gachon University ]
• Sang-Woong Lee [ Department of AI Software Gachon University ] Corresponding author

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