Evaluating unsupervised contrastive learning framework for MRI sequences classification
This work addresses the challenge of MRI sequence identification for radiologists, but it is incremental as it applies an existing unsupervised contrastive learning method to a new domain-specific task.
The authors tackled the problem of automatically identifying MRI sequences to streamline clinical workflows by proposing an unsupervised contrastive deep learning framework, achieving a classification accuracy of over 0.95 across nine common MRI sequence types.
The automatic identification of Magnetic Resonance Imaging (MRI) sequences can streamline clinical workflows by reducing the time radiologists spend manually sorting and identifying sequences, thereby enabling faster diagnosis and treatment planning for patients. However, the lack of standardization in the parameters of MRI scans poses challenges for automated systems and complicates the generation and utilization of datasets for machine learning research. To address this issue, we propose a system for MRI sequence identification using an unsupervised contrastive deep learning framework. By training a convolutional neural network based on the ResNet-18 architecture, our system classifies nine common MRI sequence types as a 9-class classification problem. The network was trained using an in-house internal dataset and validated on several public datasets, including BraTS, ADNI, Fused Radiology-Pathology Prostate Dataset, the Breast Cancer Dataset (ACRIN), among others, encompassing diverse acquisition protocols and requiring only 2D slices for training. Our system achieves a classification accuracy of over 0.95 across the nine most common MRI sequence types.