# Improved Brain Age Estimation with Slice-based Set Networks

arXiv: https://arxiv.org/abs/2102.04438 · HTML: https://mlsanity.com/paper/2102.04438/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 8 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks](https://mlsanity.com/paper/1810.00825/) (2018)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker](https://mlsanity.com/paper/1612.02572/) (2016)
- [Scanner Invariant Representations for Diffusion MRI Harmonization](https://mlsanity.com/paper/1904.05375/) (2019)
- [Towards Alzheimer's Disease Classification through Transfer Learning](https://mlsanity.com/paper/1711.11117/) (2017)

## Built on by (most cited first, 11 of 11)

- [Membership Inference Attacks on Deep Regression Models for Neuroimaging](https://mlsanity.com/paper/2105.02866/) (2021)
- [Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption](https://mlsanity.com/paper/2108.03437/) (2021)
- [Towards Sparsified Federated Neuroimaging Models via Weight Pruning](https://mlsanity.com/paper/2208.11669/) (2022)
- [Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models](https://mlsanity.com/paper/2303.01491/) (2023)
- [Efficient brain age prediction from 3D MRI volumes using 2D projections](https://mlsanity.com/paper/2211.05762/) (2022)
- [Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasets](https://mlsanity.com/paper/2407.04808/) (2024)
- [Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume](https://mlsanity.com/paper/2408.15958/) (2024)
- [Secure & Private Federated Neuroimaging](https://mlsanity.com/paper/2205.05249/) (2022)
- [scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2502.02630/) (2025)
- [FedDAPL: Toward Client-Private Generalization in Federated Learning](https://mlsanity.com/paper/2509.23688/) (2025)
- [Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRI](https://mlsanity.com/paper/2606.26894/) (2026)

## Abstract (arXiv)

Deep Learning for neuroimaging data is a promising but challenging direction. The high dimensionality of 3D MRI scans makes this endeavor compute and data-intensive. Most conventional 3D neuroimaging methods use 3D-CNN-based architectures with a large number of parameters and require more time and data to train. Recently, 2D-slice-based models have received increasing attention as they have fewer parameters and may require fewer samples to achieve comparable performance. In this paper, we propose a new architecture for BrainAGE prediction. The proposed architecture works by encoding each 2D slice in an MRI with a deep 2D-CNN model. Next, it combines the information from these 2D-slice encodings using set networks or permutation invariant layers. Experiments on the BrainAGE prediction problem, using the UK Biobank dataset, showed that the model with the permutation invariant layers trains faster and provides better predictions compared to other state-of-the-art approaches.