论文标题
使用时间分布式多模态3D CNN的高度准确的fMRI ADHD分类
Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs
论文作者
论文摘要
这项工作提出了一种用于ADHD疾病分类的fMRI数据分析算法。通过3D卷积神经网络（CNN）对fMRI的分析有了一些突破。通过这些新技术，可以保留fMRI数据的3D空间数据。此外，最近在使用3D生成对抗神经网络（GAN）来生成正常MRI数据方面已经取得了进步。这项工作利用了来自3D GAN的数据增强的多模态3D CNN，用于fMRI的ADHD预测。通过利用3D-GAN，可以使用DeepFake数据来增强3D CNN脑疾病分类的准确性。将在分布式分类的时间分布式单模式3D CNN模型和具有MRI数据的修改的多模态模型之间进行比较。
This work proposes an algorithm for fMRI data analysis for the classification of ADHD disorders. There have been several breakthroughs in the analysis of fMRI via 3D convolutional neural networks (CNNs). With these new techniques it is possible to preserve the 3D spatial data of fMRI data. Additionally there have been recent advances in the use of 3D generative adversarial neural networks (GANs) for the generation of normal MRI data. This work utilizes multi modal 3D CNNs with data augmentation from 3D GAN for ADHD prediction from fMRI. By leveraging a 3D-GAN it would be possible to use deepfake data to enhance the accuracy of 3D CNN classification of brain disorders. A comparison will be made between a time distributed single modal 3D CNN model for classification and the modified multi modal model with MRI data as well.
