# Population Template-Based Brain Graph Augmentation for Improving One-Shot Learning Classification

arXiv: https://arxiv.org/abs/2212.07790 · HTML: https://mlsanity.com/paper/2212.07790/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 14 Dec 2022.

## Builds on (most cited first, 15 of 15)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [G-Mixup: Graph Data Augmentation for Graph Classification](https://mlsanity.com/paper/2202.07179/) (2022)
- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification](https://mlsanity.com/paper/1604.07528/) (2016)
- [Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks](https://mlsanity.com/paper/1810.11730/) (2018)
- [Data Augmentation for Graph Classification](https://mlsanity.com/paper/2009.09863/) (2020)
- [Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates](https://mlsanity.com/paper/2012.14131/) (2020)
- [Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer](https://mlsanity.com/paper/2009.11166/) (2020)
- [MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations](https://mlsanity.com/paper/2104.03895/) (2021)
- [Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network](https://mlsanity.com/paper/2105.02565/) (2021)
- [Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates](https://mlsanity.com/paper/2110.03453/) (2021)
- [One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction](https://mlsanity.com/paper/2110.11238/) (2021)
- [Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data](https://mlsanity.com/paper/2109.02248/) (2021)

## Built on by (most cited first, 1 of 1)

- [Multi-Sensory Cognitive Computing for Learning Population-level Brain Connectivity](https://mlsanity.com/paper/2508.11436/) (2025)

## Abstract (arXiv)

The challenges of collecting medical data on neurological disorder diagnosis problems paved the way for learning methods with scarce number of samples. Due to this reason, one-shot learning still remains one of the most challenging and trending concepts of deep learning as it proposes to simulate the human-like learning approach in classification problems. Previous studies have focused on generating more accurate fingerprints of the population using graph neural networks (GNNs) with connectomic brain graph data. Thereby, generated population fingerprints named connectional brain template (CBTs) enabled detecting discriminative bio-markers of the population on classification tasks. However, the reverse problem of data augmentation from single graph data representing brain connectivity has never been tackled before. In this paper, we propose an augmentation pipeline in order to provide improved metrics on our binary classification problem. Divergently from the previous studies, we examine augmentation from a single population template by utilizing graph-based generative adversarial network (gGAN) architecture for a classification problem. We benchmarked our proposed solution on AD/LMCI dataset consisting of brain connectomes with Alzheimer's Disease (AD) and Late Mild Cognitive Impairment (LMCI). In order to evaluate our model's generalizability, we used cross-validation strategy and randomly sampled the folds multiple times. Our results on classification not only provided better accuracy when augmented data generated from one sample is introduced, but yields more balanced results on other metrics as well.