Deep-learning-based super-resolution photoacoustic angiography (PAA) is a powerful tool that restores blood vessel images from under-sampled images to facilitate disease diagnosis. Nonetheless, due to the scarcity of training samples, PAA super-resolution models often exhibit inadequate generalization capabilities, particularly in the context of continuous monitoring tasks. To address this challenge, we propose a novel approach that employs a super-resolution PAA method trained with forged PAA images. We start by generating realistic PAA images of human lips from hand-drawn curves using a diffusion-based image generation model. Subsequently, we train a self-similarity-based super-resolution model with these forged PAA images. Experimental results show that our method outperforms the super-resolution model trained with authentic PAA images in both original-domain and cross-domain tests. Specially, our approach boosts the quality of super-resolution reconstruction using the images forged by the deep learning model, indicating that the collaboration between deep learning models can facilitate generalization, despite limited initial dataset. This approach shows promising potential for exploring zero-shot learning neural networks for vision tasks.
翻译:基于深度学习的光声血管造影图像超分辨率是一种强大的工具,可以从欠采样的图像中恢复血管图像,以促进疾病诊断。然而,由于训练样本的稀缺性,光声血管造影图像超分辨率模型通常表现出不足的泛化能力,特别是在持续监测任务的情况下。为解决这个挑战,我们提出了一种新方法,该方法使用伪造的光声血管造影图像对超分辨率模型进行训练。我们首先使用基于扩散的图像生成模型生成人唇的逼真光声血管造影图像,然后使用这些伪造的图像训练了一个基于自相似性的超分辨率模型。实验结果表明,我们的方法在原始域和交叉域测试中都优于使用真实光声血管造影图像训练的超分辨率模型。特别地,我们的方法通过由深度学习模型伪造的图像提高了超分辨率重构的质量,表明深度学习模型之间的合作可以促进泛化能力,尽管初始数据集有限。该方法显示了探究零样学习神经网络进行视觉任务的有前途的潜力。