# Efficient brain age prediction from 3D MRI volumes using 2D projections

arXiv: https://arxiv.org/abs/2211.05762 · HTML: https://mlsanity.com/paper/2211.05762/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker](https://mlsanity.com/paper/1612.02572/) (2016)
- [Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift](https://mlsanity.com/paper/1801.05134/) (2018)
- [Augmentor: An Image Augmentation Library for Machine Learning](https://mlsanity.com/paper/1708.04680/) (2017)
- [Improved Brain Age Estimation with Slice-based Set Networks](https://mlsanity.com/paper/2102.04438/) (2021)

## Built on by (most cited first, 2 of 2)

- [Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasets](https://mlsanity.com/paper/2407.04808/) (2024)
- [Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRI](https://mlsanity.com/paper/2606.26894/) (2026)

## Abstract (arXiv)

Using 3D CNNs on high resolution medical volumes is very computationally demanding, especially for large datasets like the UK Biobank which aims to scan 100,000 subjects. Here we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of the 3D volumes leads to reasonable test accuracy when predicting the age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20 - 50 seconds using a single GPU, which two orders of magnitude faster compared to a small 3D CNN. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.