论文标题
胎头超声成像合成质量图像评估的经验研究
Empirical Study of Quality Image Assessment for Synthesis of Fetal Head Ultrasound Imaging with DCGANs
论文作者
论文摘要
在这项工作中，我们提出了一项关于DCGAN的经验研究，包括超参数启发式方法和图像质量评估，是一种解决数据集的稀缺性，以研究胎儿头超声。我们提出了实验，以显示不同图像分辨率，时期，数据集大小输入和对四个指标质量图像评估的学习率的影响：共同信息（MI），FRéchet成立距离（FID），峰值信号 - 信号 - 噪声效果比（PSNR）和本地binary Pattern vector（LBPV）。结果表明，FID和LBPV与临床图像质量评分具有更强的关系。重现这项工作的资源可在\ url {https://github.com/budai4medtech/miua2022}中获得。
In this work, we present an empirical study of DCGANs, including hyperparameter heuristics and image quality assessment, as a way to address the scarcity of datasets to investigate fetal head ultrasound. We present experiments to show the impact of different image resolutions, epochs, dataset size input, and learning rates for quality image assessment on four metrics: mutual information (MI), Fréchet inception distance (FID), peak-signal-to-noise ratio (PSNR), and local binary pattern vector (LBPv). The results show that FID and LBPv have stronger relationship with clinical image quality scores. The resources to reproduce this work are available at \url{https://github.com/budai4medtech/miua2022}.
