Abstract
To address the problem of medical image recognition, computer vision techniques 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, we conduct extensive empirical studies on video recognition techniques for MRI classification to answer the questions: (1) can we directly use video recognition models for MRI classification, (2) which model is more appropriate for MRI, (3) are the common tricks like data augmentation in video recognition still useful for MRI classification? Our work suggests that advanced video techniques benefit MRI classification. In this paper, four datasets of Alzheimer's and Parkinson's disease recognition are utilized in experiments, together with three alternative video recognition models and data augmentation techniques that are frequently applied to video tasks. In terms of efficiency, the results reveal that the video framework performs better than 3D-CNN models by 5% - 11% with 50% - 66% less trainable parameters. This report pushes forward the potential fusion of 3D medical imaging and video understanding research.
Abstract (translated)
为了解决医学图像识别问题,像卷积神经网络(CNN)这样的计算机视觉技术经常被使用。最近,基于3D CNN模型的分类器在磁共振成像(MRI)分析领域占据了主导地位。由于MRI数据和视频之间的高度相似性,我们进行了广泛的实证研究,以研究视频分类技术对MRI分类的影响,以回答以下问题:(1)我们可以直接使用视频分类模型进行MRI分类吗?(2)哪种模型更适合MRI分类?(3)视频识别中的常见的增强技巧,如数据增强,对于MRI分类仍然有用吗?我们的研究表明,先进的视频技术有助于MRI分类。在本文中,使用阿尔茨海默病和帕金森病识别的四个数据集,以及三个不同的视频分类模型和常用的视频任务增强技术。从效率上来看,结果表明,视频框架比3D-CNN模型表现更好,下降了5%至11%,训练参数少50%至66%。本报告推进了3D医学成像和视频理解研究的 potential fusion。
URL
https://arxiv.org/abs/2302.12688
