Brain disease classification method based on 3D attention convolution and self-supervised learning

A supervised learning and disease classification technology, applied in the field of brain science research, can solve problems such as high feature dimension, high-dimensional data mining, and inability to reflect the spatial relationship of voxel data, and achieve the effect of reasonable and reliable methods and improved performance

Pending Publication Date: 2022-01-21
BEIJING UNIV OF TECH
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Problems solved by technology

However, one-dimensional data cannot reflect the spatial relationship of voxel data, while 3DCNN can handle spatial data
However, human brain data has the characteristics of small sample size and high feature dimension.

Method used

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  • Brain disease classification method based on 3D attention convolution and self-supervised learning
  • Brain disease classification method based on 3D attention convolution and self-supervised learning
  • Brain disease classification method based on 3D attention convolution and self-supervised learning

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Embodiment Construction

[0060] Take the real fMRI data set as an example below to illustrate the specific implementation steps of the present invention:

[0061] Step (1) Obtain resting state fMRI data and preprocess:

[0062] Step (1.1) Resting-state fMRI data acquisition: Autism spectrum disorder (ASD) data was acquired from ABIDE (Autism Brain Imaging DataExchange, http: / / fcon_1000.projects.nitrc.org / indi / abide / ) for analysis; ABIDE-I contains rs-fMRI (Resting-state functional magnetic resonance imaging) data of 1112 subjects from 17 different sites; the quality of these data was assessed based on the results of visual inspection by three human experts, by incomplete Brain coverage, high motor peaks, ghosting, and other scanner artifacts generated 871 subjects from an initial sample of 1112 subjects, including 468 normal controls and 403 ASD; ABIDE-II collected The data of 1114 subjects from 17 different sites, 518 subjects were selected as the experimental data, including 265 people in the norma...

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Abstract

The invention discloses a brain disease classification method based on 3D attention convolution and self-supervised learning, and belongs to the field of brain science research. The method specifically comprises the following steps: acquiring resting-state fMRI data and preprocessing; constructing functional connection data based on the fMRI whole brain voxels; dividing a data set; and performing brain disease classification based on attention convolution and self-supervised learning. According to the method, spatial features of whole-brain voxels are extracted from fMRI data by using 3DCNN of an attention mechanism, meanwhile, more meaningful characterization is mined by using self-supervised learning, and finally, combined training is performed on a classification task and a self-supervised auxiliary task to optimize parameters. The method provided by the invention can better explore the spatial information of the brain and mine the implicit features of the data, so that the classification effect is improved, and the method is reasonable and reliable.

Description

technical field [0001] The invention belongs to the field of brain science research, in particular, the invention relates to a brain disease classification method based on 3D attention convolution and self-supervised learning. Background technique [0002] The human brain is a highly complex system of the human body, containing a large number of neuron cells. Through the interaction between multiple neurons, neuron clusters or multiple brain regions, the human brain can complete various complex tasks. The structure and function of the human brain are extremely complex, far beyond our current cognitive capabilities. Therefore, it is undoubtedly very meaningful to explore and understand the working mechanism of the human brain and unravel the mystery of the brain. The classification of brain diseases based on fMRI data is an important research direction in the field of brain science. Analysis of fMRI data is helpful for understanding autism spectrum disorder (Autism spectrum...

Claims

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Application Information

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IPC IPC(8): A61B5/00A61B5/055G06V10/764G06V10/774G06V10/82G06K9/62G06N3/04G06N3/08
CPCA61B5/4064A61B5/7264A61B5/7267A61B5/055G06N3/08G06N3/045G06F18/24G06F18/214Y02A90/10
Inventor 冀俊忠于乐雷名龙
Owner BEIJING UNIV OF TECH
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