ABCDEFGH：一种基于自适应的卷积神经网络-CycleGAN疾病病程演化框架，在健康教育中使用生成模型
ABCDEFGH: An Adaptation-Based Convolutional Neural Network-CycleGAN Disease-Courses Evolution Framework Using Generative Models in Health Education
AI总结：
本文提出ABCDEFGH框架，利用CNN与CycleGAN生成合成医学图像，以解决医学教育中高质量教学数据因隐私和资源不足而短缺的问题。
摘要：
随着现代医学的进步以及MRI、CT和细胞分析等技术的发展，临床医生准确解读各种诊断图像变得愈发重要。然而，由于隐私问题和教育资源短缺，现代医学教育常常面临难以获取高质量教学材料的挑战（Balogh等，2015）。在此背景下，由机器学习模型（尤其是生成模型）生成的图像数据提供了一种有前景的解决方案。这些模型能够在不泄露患者隐私的情况下创建多样且可比较的影像数据集，从而支持现代医学教育。在本研究中，我们探索使用卷积神经网络（CNNs）和CycleGAN（Zhu等，2017）来生成合成医学图像。源代码可在https://github.com/mliuby/COMP4211-Project获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
