# Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data

arXiv: https://arxiv.org/abs/1708.00587 · HTML: https://mlsanity.com/paper/1708.00587/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 8 of 8)

- [Graph Neural Networks in Network Neuroscience](https://mlsanity.com/paper/2106.03535/) (2021)
- [Automatic Landmark Detection and Registration of Brain Cortical Surfaces via Quasi-Conformal Geometry and Convolutional Neural Networks](https://mlsanity.com/paper/2208.07010/) (2022)
- [SurfGNN: A robust surface-based prediction model with interpretability for coactivation maps of spatial and cortical features](https://mlsanity.com/paper/2411.05825/) (2024)
- [An explainability framework for cortical surface-based deep learning](https://mlsanity.com/paper/2203.08312/) (2022)
- [NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants](https://mlsanity.com/paper/2301.00815/) (2023)
- [Utility of Equivariant Message Passing in Cortical Mesh Segmentation](https://mlsanity.com/paper/2206.03164/) (2022)
- [JParc: Joint cortical surface parcellation with registration](https://mlsanity.com/paper/2512.22485/) (2025)
- [Geometric Mean Pooling for Equal-Weight Multiplicative Coarse-Graining](https://mlsanity.com/paper/2609.21876/) (2026)

## Abstract (arXiv)

The conventional CNN, widely used for two-dimensional images, however, is not directly applicable to non-regular geometric surface, such as a cortical thickness. We propose Geometric CNN (gCNN) that deals with data representation over a spherical surface and renders pattern recognition in a multi-shell mesh structure. The classification accuracy for sex was significantly higher than that of SVM and image based CNN. It only uses MRI thickness data to classify gender but this method can expand to classify disease from other MRI or fMRI data