论文标题
神经发育表型预测：最先进的深度学习模型
Neurodevelopmental Phenotype Prediction: A State-of-the-Art Deep Learning Model
论文作者
论文摘要
医学图像分析中的一个主要挑战是对神经影像数据的生物标志物的自动检测。通常基于图像注册的传统方法在捕获跨个体皮质组织的高变异性方面受到限制。深度学习方法已被证明在克服这一困难方面取得了成功，其中一些人甚至在某些数据集上表现优于医疗专业人员。在本文中，我们应用了深层神经网络来分析新生儿的皮质表面数据，这些新生儿的皮质表面数据是从公开开发的人类连接组项目（DHCP）中得出的。我们的目标是确定神经发育生物标志物，并根据这些生物标志物预测出生时的胎龄。利用对术语等效年龄的早产新生儿的扫描，我们能够研究早产对妊娠晚期皮质生长和成熟的影响。除了达到最新的预测准确性外，所提出的模型的参数比基线少得多，并且其误差在未注册和注册的皮质表面上保持较低。
A major challenge in medical image analysis is the automated detection of biomarkers from neuroimaging data. Traditional approaches, often based on image registration, are limited in capturing the high variability of cortical organisation across individuals. Deep learning methods have been shown to be successful in overcoming this difficulty, and some of them have even outperformed medical professionals on certain datasets. In this paper, we apply a deep neural network to analyse the cortical surface data of neonates, derived from the publicly available Developing Human Connectome Project (dHCP). Our goal is to identify neurodevelopmental biomarkers and to predict gestational age at birth based on these biomarkers. Using scans of preterm neonates acquired around the term-equivalent age, we were able to investigate the impact of preterm birth on cortical growth and maturation during late gestation. Besides reaching state-of-the-art prediction accuracy, the proposed model has much fewer parameters than the baselines, and its error stays low on both unregistered and registered cortical surfaces.
