A GAN Model with Controllable Lesion Generation for Synthetic Capsule Endoscopy Datasets
Keywords: CycleGAN, Crop and Paste, Controllable Lesion Generation, Synthetic Capsule Endoscopy, Data Imbalance
TL;DR: A GAN model for precise lesion generation in synthetic capsule endoscopy data, tackling data imbalance and enhancing training data quality.
Abstract: In this paper, we will address a novel approach to create a synthetic capsule endoscopy dataset. In the medical area, research using deep learning has been actively conducted. It is important to secure a large amount of high-quality datasets to develop a deep learning model. However, medical data have privacy concerns or data bias issues. For this reason, medical data for learning can be noisy and incomplete. Also, it is difficult to obtain qualitative and quantitative medical data. To overcome these limitations, one of the studies that has recently been in the spotlight is synthetic data research. If we use synthetic data to learn deep learning models, we can maintain a more uniform data format and label. In this study, we want to solve the problem of lack of data by creating enough endoscopic datasets by naturally synthesizing the desired lesions in the desired location. We applied the crop and paste method and CycleGAN to the capsule endoscopy dataset for the first time. After placing the desired lesion at the desired coordinates using the crop and paste method, a widely used Data Augmentation Technique, we achieve natural synthesis using the CycleGAN model. We propose an Image-to-Image model that adjusts the type of location and lesion of the generated synthetic data. Through high-quality synthetic data generated in this way, we aim to realize the potential of deep learning in the medical field.
Submission Number: 40
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