用生成模型合成医学影像，解决教学资源少、隐私难保障问题
ABCDEFGH: An Adaptation-Based Convolutional Neural Network-CycleGAN Disease-Courses Evolution Framework Using Generative Models in Health Education
- 结合CNN与CycleGAN，基于真实影像生成多样化合成数据
- 生成图像可模拟疾病发展过程，支持医学教育可视化教学
- 适合医学教育者、生成模型研究者参考，提升教学资源可及性
随着现代医学的发展及MRI、CT和细胞分析等技术的进步，临床医生准确解读各类诊断图像变得愈发重要。然而，现代医学教育常受限于高质量教学材料的缺乏，主要源于隐私顾虑和教育资源不足（Balogh et al., 2015）。在此背景下，机器学习模型尤其是生成模型所生成的图像数据成为可行解决方案。这些模型可在不泄露患者隐私的前提下，生成多样且具有可比性的医学影像数据集，从而支持现代医学教育。本研究探索了卷积神经网络（CNN）与CycleGAN（Zhu et al., 2017）在生成合成医学影像中的应用。源代码已公开于 https://github.com/mliuby/COMP4211-Project。
原文摘要 · Abstract (English)
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.
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