Deep neural networks (DNN) have been designed to predict the chronological age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as a valuable biomarker for the early detection of development-related or aging-related disorders. Recent DNN models for brain age estimations usually rely too much on large sample sizes and complex network structures for multi-stage feature refinement. However, in clinical application scenarios, researchers usually cannot obtain thousands or tens of thousands of MRIs in each data center for thorough training of these complex models. This paper proposes a simple fully convolutional network (SFCNeXt) for brain age estimation in small-sized cohorts with biased age distributions. The SFCNeXt consists of Single Pathway Encoded ConvNeXt (SPEC) and Hybrid Ranking Loss (HRL), aiming to estimate brain ages in a lightweight way with a sufficient exploration of MRI, age, and ranking features of each batch of subjects. Experimental results demonstrate the superiority and efficiency of our approach.

翻译：深度神经网络（DNN）已被设计用于从T1加权磁共振图像中预测健康大脑的时序年龄，预测的脑龄可作为早期检测发育相关或衰老相关疾病的生物标志物。当前用于脑龄估计的DNN模型通常过度依赖大规模样本量和复杂网络结构进行多阶段特征细化。然而在临床应用中，研究人员通常无法在每个数据中心获取数千甚至数万份MRI数据来充分训练这些复杂模型。本文提出了一种简单全卷积网络（SFCNeXt），用于年龄分布存在偏差的小规模队列的脑龄估计。SFCNeXt由单路径编码ConvNeXt（SPEC）和混合排序损失（HRL）组成，旨在以轻量化方式充分挖掘每批受试者的MRI特征、年龄特征和排序特征来实现脑龄估计。实验结果表明了该方法的优越性和高效性。