Thresholding method based on weight distribution

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...

AI Technical Summary

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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