mlsanityData to 7 Oct 2026

Improved Brain Age Estimation with Slice-based Set Networks

arXiv 2102.04438 ↗First posted 8 Feb 2021eess.IV cs.LG q-bio.QMcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 8 Feb 2021.

Builds on

6 of 21 references.

References · 6

  1. 2017Deep Setscited by 1,394
  2. 2018Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networkscited by 129
  3. 20203D Deep Learning on Medical Images: A Reviewcited by 76
  4. 2016Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarkercited by 72
  5. 2019Scanner Invariant Representations for Diffusion MRI Harmonizationcited by 27
  6. 2017Towards Alzheimer's Disease Classification through Transfer Learningcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 2; 2025: 2; 2026: 1.

11 papers.

  1. 2021Membership Inference Attacks on Deep Regression Models for Neuroimagingcited by 19
  2. 2021Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryptioncited by 18
  3. 2022Towards Sparsified Federated Neuroimaging Models via Weight Pruningcited by 3
  4. 2023Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Modelscited by 3
  5. 2022Efficient brain age prediction from 3D MRI volumes using 2D projectionscited by 2
  6. 2024Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasetscited by 2
  7. 2024Efficient Slice Anomaly Detection Network for 3D Brain MRI Volumecited by 1
  8. 2022Secure & Private Federated Neuroimagingcited by 0
  9. 2025scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosiscited by 0
  10. 2025FedDAPL: Toward Client-Private Generalization in Federated Learningcited by 0
  11. 2026Modeling Local, Global, and Cross-Modal Context in Multimodal 3D MRIcited by 0