Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data
This work addresses the challenge of analyzing surface-based neuroimaging data for researchers in neuroscience and medical imaging, representing an incremental improvement with a novel method for a known bottleneck.
The authors tackled the problem of applying convolutional neural networks to non-regular geometric surfaces like cortical thickness data by proposing a Geometric CNN (gCNN) for spherical surface representation and multi-shell mesh structures, achieving significantly higher classification accuracy for sex compared to SVM and image-based CNN.
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