用改进GAN生成高保真功能超声图像，解决数据少难题
UltraDfeGAN: Detail-Enhancing Generative Adversarial Networks for High-Fidelity Functional Ultrasound Synthesis
- 设计增强特征与归一化模块的GAN架构
- 生成图像在多场景下保持高保真度与生理合理性
- 合成数据提升下游分类任务准确率，适合医学影像研究
功能超声（fUS）是一种具有高时空分辨率的神经成像技术，可通过神经血管耦合非侵入式观察脑活动。尽管在新生儿监测和术中导航等临床应用中有潜力，但其发展受限于数据稀缺及真实fUS图像生成困难。本文提出一种针对fUS图像合成的生成对抗网络（GAN）框架，通过引入特征增强模块和归一化技术，提升生成图像的保真度与生理合理性。实验在公开fUS数据集上进行，结果表明该框架在多种条件下均能生成高质量图像，并在下游任务中作为数据增强手段显著提高分类准确率，有效缓解数据不足问题。
原文摘要 · Abstract (English)
Functional ultrasound (fUS) is a neuroimaging technique known for its high spatiotemporal resolution, enabling non-invasive observation of brain activity through neurovascular coupling. Despite its potential in clinical applications such as neonatal monitoring and intraoperative guidance, the development of fUS faces challenges related to data scarcity and limitations in generating realistic fUS images. This paper explores the use of a generative adversarial network (GAN) framework tailored for fUS image synthesis. The proposed method incorporates architectural enhancements, including feature enhancement modules and normalization techniques, aiming to improve the fidelity and physiological plausibility of generated images. The study evaluates the performance of the framework against existing generative models, demonstrating its capability to produce high-quality fUS images under various experimental conditions. Additionally, the synthesized images are assessed for their utility in downstream tasks, showing improvements in classification accuracy when used for data augmentation. Experimental results are based on publicly available fUS datasets, highlighting the framework's effectiveness in addressing data limitations.
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