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To address the problem of medical image recognition, computer visiontechniques like convolutional neural networks (CNN) are frequently used.Recently, 3D CNN-based models dominate the field of magnetic resonance image(MRI) analytics. Due to the high similarity between MRI data and videos, weconduct extensive empirical studies on video recognition techniques for MRIclassification to answer the questions: (1) can we directly use videorecognition models for MRI classification, (2) which model is more appropriatefor MRI, (3) are the common tricks like data augmentation in video recognitionstill useful for MRI classification? Our work suggests that advanced videotechniques benefit MRI classification. In this paper, four datasets ofAlzheimer's and Parkinson's disease recognition are utilized in experiments,together with three alternative video recognition models and data augmentationtechniques that are frequently applied to video tasks. In terms of efficiency,the results reveal that the video framework performs better than 3D-CNN modelsby 5% - 11% with 50% - 66% less trainable parameters. This report pushesforward the potential fusion of 3D medical imaging and video understandingresearch.

Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks