Thresholding method based on weight distribution

A weight and threshold technology, applied in the field of thresholding based on weight distribution, can solve the problems of determining or defining thresholds or ratios, wasting calculation costs, etc., and achieve the effect of retaining diversity

Active Publication Date: 2019-09-06
ANHUI NORMAL UNIV
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Problems solved by technology

In addition, for single threshold or proportional sparse methods, there is still no good standard to determine or define the threshold or ratio
In practice, it is common to try all possible values ​​to explore an optimal threshold or ratio, which is computationally expensive

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  • Thresholding method based on weight distribution
  • Thresholding method based on weight distribution
  • Thresholding method based on weight distribution

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Embodiment Construction

[0027] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0028] figure 1 The flow chart of the thresholding method based on weight distribution provided by the embodiment of the present invention, the method specifically includes the following steps:

[0029] S1. Divide the training set samples into two sample groups based on the class label, namely the patient group and the normal group;

[0030] S2. For all samples in the two sample groups, use the Pearson correlation coefficient to construct the functional connection between the brain regions of each sample, and use the Pearson correlation coefficient between the time series of each brain region a...

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Abstract

The invention is suitable for the field of machine learning and medical image processing technology, and provides a thresholding method based on weight distribution. The method comprises the followingsteps of S1, dividing a training set sample to two sample sets based on a class label, namely a patient set and a normal person set; S2, for aiming at all samples in the two sample sets, using a Pearson correlation coefficient between time sequences of brain regions as a connecting weight, and constructing a brain network adjacent matrix; and S3, based on the function connecting distribution difference of the two sample sets, constructing a threshold for aiming at each brain region. Through each function connection in the network, the weight distribution is used for adaptively constructing anoptimal threshold, thereby constructing different thresholds for the function connection between different brain regions, thereby constructing a threshold matrix for the whole brain network, thresholding the brain network, keeping function connection diversity between the different brain regions, and settling defects in a single threshold or percentage method.

Description

technical field [0001] The invention belongs to the technical field of machine learning and medical image processing, and provides a thresholding method based on weight distribution. Background technique [0002] The current research results show that the human brain is a complex system based on the division of functional connections in various brain regions. It is usually abstracted into a brain network, that is, each brain region constitutes the vertices of each network, and the connection strength of each brain region forms the edges of the brain network. Brain network analysis provides an effective way for humans to explore the structure and function of brain tissue and predict some differences in structure and function caused by some brain diseases. At present, researchers have studied brain diseases such as Alzheimer's disease (AD) and pre-morbidity mild cognitive impairment (MCI), attention deficit hyperactivity disorder (Attention Deficit Hyperactivity Disorder, ADH...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H30/20G06K9/32G06K9/62
CPCG16H30/20G06V10/25G06F18/2411G06F18/214
Inventor 接标王正东卞维新丁新涛周文左开中陈付龙罗永龙
Owner ANHUI NORMAL UNIV
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