Deep learning pipeline for brain MRI acquisition type classification

Abstract

We have developed an effective deep learning pipeline to classify brain magnetic resonance imaging scans automatically into 12 subcategories. The classification is performed by a meta classifier which receives level one predictions from Microsoft's Residual Networks (ResNet), Google’s Neural Architecture Search Network (NASNet) and a text-based classifier on DICOM header series description and combine them to get final classification. The overall classifier was trained, validated and tested on 2750 MRI images from multicenter projects. The classifier was packaged using Docker containerization technology and deployed on a local XNAT instance and tested on 3000 independent imaging sessions with 98.5% accuracy.