ABCDEFGH: An Adaptation-Based Convolutional Neural Network-CycleGAN Disease-Courses Evolution Framework Using Generative Models in Health Education

TL;DR

This paper proposes a framework combining adaptation-based CNNs and CycleGAN to generate synthetic medical images, aiming to enhance medical education by providing diverse, privacy-preserving datasets.

Contribution

It introduces a novel framework integrating CNNs and CycleGAN for medical image synthesis to address educational resource limitations.

Findings

Generated images are realistic and diverse.

Framework effectively preserves privacy while creating useful datasets.

Potential to improve medical training resources.

Abstract

With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy concerns and a shortage of educational resources (Balogh et al., 2015). In this context, image data generated by machine learning models, particularly generative models, presents a promising solution. These models can create diverse and comparable imaging datasets without compromising patient privacy, thereby supporting modern medical education. In this study, we explore the use of convolutional neural networks (CNNs) and CycleGAN (Zhu et al., 2017) for generating synthetic medical images. The source code is available at https://github.com/mliuby/COMP4211-Project.