FAM-Former: Global Transformer and Feature Aggregation Module for Knee MRI Age Estimation

Linchao He, Haozhe Zhu, Xiaodong Deng, Fei Fan, Peixi Liao, Wenchao Du, Hu Chen, Zhenhua Deng, Yi Zhang

Abstract: Age estimation is a significant and active research field in radiological medicine. Recently, researchers have turned their attention to magnetic resonance imaging (MRI) for age estimation, primarily due to concerns regarding ionizing radiation in traditional imaging techniques. In this article, based on the knee MRI dataset, we present a novel end-to-end network that combines convolutional neural network (CNN) and visual transformer network to extract complementary features. In addition, we introduce a feature aggregation module (FAM) that aggregates features from different local knee MRI slices. Our proposed method integrates the feature maps from CNN with the patch embeddings of visual transformer branches, allowing for the acquisition of both local and global information necessary for extracting age-related features. Moreover, we introduce an FAM utilizing the graph attention network (GAT) to achieve interaction between multiple slice features at the feature level. Through extensive experiments conducted on a dataset of 80 knee MRI samples spanning ages 12.0–25.9 years, our method has demonstrated state-of-the-art performance in knee MRI age estimation. Specifically, in a fivefold cross-validation setup, we achieved a mean absolute error (MAE) of 1.52 ± 1.27 years in age regression.