Scholarlink

<p>Deep learning algorithms trained on medical images often encounter limited data availability, leading to overfitting and imbalanced datasets. Synthetic datasets can address these challenges by providing a priori control over dataset size and balance. In this study, we present PACGAN (Progressive Auxiliary Classifier Generative Adversarial Network), a proof-of-concept framework that effectively combines Progressive Growing GAN and Auxiliary Classifier GAN (ACGAN) to generate high-quality, class-specific synthetic medical images. PACGAN leverages latent space information to perform conditional synthesis of high-resolution brain magnetic resonance (MR) images, specifically targeting Alzheimer’s disease patients and healthy controls. Trained on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, PACGAN demonstrates its ability to generate realistic synthetic images, which are assessed for quality using quantitative metrics. The ability of the generator to perform proper target synthesis of the two classes was also assessed by evaluating the performance of the pre-trained discriminator when classifying real unseen images, which achieved an area under the receiver operating characteristic curve (AUC) of 0.813, supporting the ability of the model to capture the target characteristics of each class. The pre-trained models of the generator and discriminator, together with the source code, are available in our repository: <a href="https://github.com/aiformedresearch/PACGAN">https://github.com/aiformedresearch/PACGAN</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

High-resolution conditional MR image synthesis through the PACGAN framework

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月1日

查看全文
  • Matteo Lai,
  • Chiara Marzi,
  • Luca Citi,
  • Stefano Diciotti

摘要

Deep learning algorithms trained on medical images often encounter limited data availability, leading to overfitting and imbalanced datasets. Synthetic datasets can address these challenges by providing a priori control over dataset size and balance. In this study, we present PACGAN (Progressive Auxiliary Classifier Generative Adversarial Network), a proof-of-concept framework that effectively combines Progressive Growing GAN and Auxiliary Classifier GAN (ACGAN) to generate high-quality, class-specific synthetic medical images. PACGAN leverages latent space information to perform conditional synthesis of high-resolution brain magnetic resonance (MR) images, specifically targeting Alzheimer’s disease patients and healthy controls. Trained on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, PACGAN demonstrates its ability to generate realistic synthetic images, which are assessed for quality using quantitative metrics. The ability of the generator to perform proper target synthesis of the two classes was also assessed by evaluating the performance of the pre-trained discriminator when classifying real unseen images, which achieved an area under the receiver operating characteristic curve (AUC) of 0.813, supporting the ability of the model to capture the target characteristics of each class. The pre-trained models of the generator and discriminator, together with the source code, are available in our repository: https://github.com/aiformedresearch/PACGAN.