Studies on classification of fMRI data using deep learning approach
Studies on classification of fMRI data using deep learning approach
复制标题
使用深度学习方法对功能磁共振成像数据进行分类的研究
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
N. K. A. Rashid
中科院分区：
文献类型：
--
作者：
Nur Farahana Mohd Suhaimi;Z. Htike;N. K. A. Rashid
大脑作为整个人体的主要服务器是一个复杂的组成部分。阅读和解读大脑是一项具有挑战性的任务。功能磁共振成像（fMRI）已成为完成这一任务的手段之一。功能磁共振成像是一种非侵入性技术，可以根据各种刺激来测量人类受试者的大脑活动。然而，每个受试者的fMRI数据集是巨大和高维的。例如，对于3D图像时间序列，数据集具有四个维度。对于具有不同解剖结构和尺寸的数据集，使用模式识别进行预处理和分析是不重要的。另一方面，采用监督学习或生物标记来降低fMRI数据集的维数。然而，这个过程是困难的和主观的标记数据集。因此，一种精通信号处理、自然语言处理（NLP）和对象识别的方法，即深度学习，被认为比通常的分类方法具有更高的标准。深度学习是神经网络的改进版本，具有更高的能力和准确性。本文旨在通过对fMRI数据分类的三项研究，对深度学习方法在fMRI分类中的应用进行综述。
Brain as main server for entire human body is a complex composition. It is a challenging task to read and interpret the brain. Functional magnetic resonance imaging (fMRI) has become one of the means to do the task. fMRI is a non-invasive technique to measure brain activity of a human subject according to various stimuli. However, the fMRI datasets for each subject is huge and high-dimensional. For instance, the dataset has four dimensions for 3D images time series. Pre-processing and analysing using pattern recognition are insignificance for datasets with varied anatomical structures and dimensions. On the other hand, supervised learning or biomarker is employed to reduce the curse-of-dimensionality of fMRI datasets. Yet, the process is difficult and subjective to the labeled datasets. Therefore, a well-versed approach in signal processing, natural language processing (NLP) and object recognition, known as deep learning is seen to have higher standard than usual classification approach. Deep learning is the improved version of neural network with higher capability and accuracy. This paper aims to review the deep learning approach in fMRI classifications based on three studies on fMRI data classification.