Department of Information Engineering, Università Politecnica delle Marche, Italy.
Department of Information Engineering, Università Politecnica delle Marche, Italy.
Comput Methods Programs Biomed. 2022 Oct;225:107057. doi: 10.1016/j.cmpb.2022.107057. Epub 2022 Aug 3.
The use of deep learning for preterm infant's movement monitoring has the potential to support clinicians in early recognizing motor and behavioural disorders. The development of deep learning algorithms is, however, hampered by the lack of publicly available annotated datasets.
To mitigate the issue, this paper presents a Generative Adversarial Network-based framework to generate images of preterm infants in a given pose. The framework consists of a bibranch encoder and a conditional Generative Adversarial Network, to generate a rough image and a refined version of it, respectively.
Evaluation was performed on the Moving INfants In RGB-D dataset which has 12.000 depth frames from 12 preterm infants. A low Fréchet inception distance (142.9) and an inception score (2.8) close to that of real-image distribution (2.6) are obtained. The results achieved show the potentiality of the framework in generating realistic depth images of preterm infants in a given pose.
Pursuing research on the generation of new data may enable researchers to propose increasingly advanced and effective deep learning-based monitoring systems.
深度学习在早产儿运动监测中的应用具有支持临床医生早期识别运动和行为障碍的潜力。然而，深度学习算法的发展受到缺乏公开可用的标注数据集的限制。
为了解决这个问题，本文提出了一种基于生成对抗网络的框架，用于生成给定姿势的早产儿图像。该框架由一个双分支编码器和一个条件生成对抗网络组成，分别生成粗糙图像和精细版本。
在 Moving INfants In RGB-D 数据集上进行了评估，该数据集包含 12 个早产儿的 12000 个深度帧。得到了较低的 Fréchet inception distance（142.9）和接近真实图像分布的 inception score（2.8）（2.6）。所取得的结果表明了该框架在生成给定姿势的早产儿真实深度图像方面的潜力。
对新数据生成的研究可能使研究人员能够提出越来越先进和有效的基于深度学习的监测系统。
