# Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss arXiv: https://arxiv.org/abs/2106.03052 ยท HTML: https://mlsanity.com/paper/2106.03052/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 10 of its 70 references that are in the map. - First posted on arXiv 6 Jun 2021. ## Builds on (most cited first, 10 of 10) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017) - [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015) - [ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks](https://mlsanity.com/paper/1908.03930/) (2019) - [Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker](https://mlsanity.com/paper/1612.02572/) (2016) - [SoDeep: a Sorting Deep net to learn ranking loss surrogates](https://mlsanity.com/paper/1904.04272/) (2019) - [Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks](https://mlsanity.com/paper/1801.04013/) (2018) ## Built on by (most cited first, 17 of 17) - [Global-Local Transformer for Brain Age Estimation](https://mlsanity.com/paper/2109.01663/) (2021) - [Deep Relation Learning for Regression and Its Application to Brain Age Estimation](https://mlsanity.com/paper/2204.06598/) (2022) - [Triamese-ViT: A 3D-Aware Method for Robust Brain Age Estimation from MRIs](https://mlsanity.com/paper/2401.09475/) (2024) - [Brain Structure Ages -- A new biomarker for multi-disease classification](https://mlsanity.com/paper/2304.06591/) (2023) - [Dual Graph Attention based Disentanglement Multiple Instance Learning for Brain Age Estimation](https://mlsanity.com/paper/2403.01246/) (2024) - [How Quality Affects Deep Neural Networks in Fine-Grained Image Classification](https://mlsanity.com/paper/2405.05742/) (2024) - [Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease](https://mlsanity.com/paper/2410.22454/) (2024) - [OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt](https://mlsanity.com/paper/2205.04684/) (2022) - [Evidence-aware multi-modal data fusion and its application to total knee replacement prediction](https://mlsanity.com/paper/2303.13810/) (2023) - [SFCNeXt: a simple fully convolutional network for effective brain age estimation with small sample size](https://mlsanity.com/paper/2305.18771/) (2023) - [Age Estimation Based on Graph Convolutional Networks and Multi-head Attention Mechanisms](https://mlsanity.com/paper/2310.08064/) (2023) - [Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model](https://mlsanity.com/paper/2310.10209/) (2023) - [Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging](https://mlsanity.com/paper/2411.10100/) (2024) - [Deeply Supervised Multi-Task Autoencoder for Biological Brain Age estimation using three dimensional T1-weighted magnetic resonance imaging](https://mlsanity.com/paper/2508.01565/) (2025) - [BrainRotViT: Transformer-ResNet Hybrid for Explainable Modeling of Brain Aging from 3D sMRI](https://mlsanity.com/paper/2511.15188/) (2025) - [An Artificial Intelligence Framework for Measuring Human Spine Aging Using MRI](https://mlsanity.com/paper/2511.17485/) (2025) - [A Two-Stage Multi-Modal MRI Framework for Lifespan Brain Age Prediction](https://mlsanity.com/paper/2604.16655/) (2026) ## Abstract (arXiv) Chronological age of healthy people is able to be predicted accurately using deep neural networks from neuroimaging data, and the predicted brain age could serve as a biomarker for detecting aging-related diseases. In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Compared with existing methods, TSAN has the following improvements. First, TSAN uses a two-stage cascade network architecture, where the first-stage network estimates a rough brain age, then the second-stage network estimates the brain age more accurately from the discretized brain age by the first-stage network. Second, to our knowledge, TSAN is the first work to apply novel ranking losses in brain age estimation, together with the traditional mean square error (MSE) loss. Third, densely connected paths are used to combine feature maps with different scales. The experiments with 6586 MRIs showed that TSAN could provide accurate brain age estimation, yielding mean absolute error (MAE) of 2.428 and Pearson's correlation coefficient (PCC) of 0.985, between the estimated and chronological ages. Furthermore, using the brain age gap between brain age and chronological age as a biomarker, Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) can be distinguished from healthy control (HC) subjects by support vector machine (SVM). Classification AUC in AD/HC and MCI/HC was 0.904 and 0.823, respectively. It showed that brain age gap is an effective biomarker associated with risk of dementia, and has potential for early-stage dementia risk screening. The codes and trained models have been released on GitHub: https://github.com/Milan-BUAA/TSAN-brain-age-estimation.