SFCNeXt: a simple fully convolutional network for effective brain age estimation with small sample size

TL;DR

This paper introduces SFCNeXt, a lightweight fully convolutional network designed for accurate brain age estimation from MRI scans, especially effective with small sample sizes and biased age distributions.

Contribution

The paper presents a novel simple fully convolutional network architecture with hybrid ranking loss for brain age estimation in small cohorts, addressing limitations of complex models requiring large datasets.

Findings

Outperforms existing methods in small sample scenarios

Efficient and lightweight model suitable for clinical settings

Demonstrates robustness with biased age distributions

Abstract

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.