CN113035349A - Neural network dynamic fusion method for genetic metabolic disease multi-center screening - Google Patents

Neural network dynamic fusion method for genetic metabolic disease multi-center screening Download PDF

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CN113035349A
CN113035349A CN202110320409.1A CN202110320409A CN113035349A CN 113035349 A CN113035349 A CN 113035349A CN 202110320409 A CN202110320409 A CN 202110320409A CN 113035349 A CN113035349 A CN 113035349A
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尹建伟
林博
舒强
李莹
邓水光
蒋萍萍
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Abstract

The invention discloses a neural network dynamic fusion method for genetic metabolic disease multi-center screening, which is characterized in that two types of nodes are required to be equipped besides a plurality of screening centers: the system comprises task nodes and computing nodes, wherein the task nodes are responsible for management, distribution and maintenance of multi-center screening tasks; each screening center needs to be provided with a computing node and is responsible for the task node to issue the computation of the joint modeling task. The method is oriented to a genetic metabolic disease multi-center screening scene, and fills the gap of a multi-center combined modeling method. Secondly, combining the characteristics of multiple genetic metabolic disease multi-center screening modeling tasks and high neural network fusion communication load pressure, the method adopts exploratory parameter sampling, evaluates the iterative synchronization degree of a plurality of computing node models, dynamically adjusts the time node of model fusion, improves the fusion efficiency, reduces the communication times and can effectively reduce the communication load of the whole task.

Description

Neural network dynamic fusion method for genetic metabolic disease multi-center screening
Technical Field
The invention belongs to the technical field of multi-center screening model construction, relates to a dynamic fusion method of a neural network, and particularly relates to a dynamic fusion method of the neural network for genetic metabolic disease multi-center screening.
Background
In recent years, the screening mode of the genetic metabolic diseases is developed from the original closed independent screening of a single center and a single hospital into the combined screening of multi-center cooperation, and various alliances such as a hospital and a medical community appear, such as a national children health and disease clinical medicine research center, a national children regional medical center and the like, so that the resource allocation, the precision and the efficiency of the genetic metabolic disease screening are enhanced. Meanwhile, the development of the artificial intelligence technology brings a revolution to the medical industry, and a new mode of intelligent medical treatment is provided. By combining intelligent algorithms such as data mining and machine learning, the genetic metabolic disease screening process based on artificial intelligence is optimized and upgraded, so that a doctor is assisted in interpretation and diagnosis, and the screening efficiency is greatly improved.
At present, some hospitals and screening centers are used as test points, artificial intelligence auxiliary diagnosis platforms are deployed to improve the screening quality of the hospitals, and two problems still exist: firstly, in a multi-center screening mode, an artificial intelligence combined modeling method facing genetic metabolic diseases is not available for a while; secondly, the neural network is a common and effective model in the existing artificial intelligence auxiliary diagnosis platform, and one bottleneck for expanding the neural network to multi-center combined modeling is the pressure of a large amount of parameter and gradient information on communication load during model fusion. Because the hereditary metabolic diseases are a large class of hereditary diseases, a plurality of clinical screening or clinical scientific research property modeling tasks exist in a scene, but the calculation resources of a hospital are objectively limited, and the communication concurrence of a large number of combined modeling tasks cannot be borne, so that the multi-center combined modeling efficiency is low.
Disclosure of Invention
The invention aims to provide a dynamic fusion method of a neural network for genetic metabolic disease multi-center screening aiming at the defects of the prior art. Secondly, combining the characteristics of multiple genetic metabolic disease multi-center screening modeling tasks and high neural network fusion communication load pressure, the method adopts exploratory parameter sampling, evaluates the iterative synchronization degree of a plurality of computing node models, dynamically adjusts the time node of model fusion, improves the fusion efficiency, reduces the communication times and can effectively reduce the communication load of the whole task.
The technical scheme adopted by the invention is as follows:
a neural network dynamic fusion method for genetic metabolic disease multi-center screening is a scene for genetic metabolic disease multi-center screening, and two types of nodes are required to be equipped besides a plurality of screening centers: the system comprises task nodes and computing nodes, wherein the task nodes are responsible for management, distribution and maintenance of a multi-center screening task, and a user directly interacts with the task nodes; each screening center needs to be provided with a computing node and is responsible for the task node to issue the computation of the joint modeling task, the computing node only interacts with the task node, and a user cannot directly access the computing node; the connection mode of the task node and the computing node is any mainstream network topology structure.
When a user initiates a genetic metabolic disease multi-center screening task T, m screening centers participating in combined modeling are required to be selected; designing the structure and hyper-parameters of a neural network model, and model training configuration including the number of iterations E of the global modelgNumber of iterations of local model ElEtc.; configuration ofParameters of the dynamic fusion method: the parameter detection ratio alpha is set to be 0, 1]The floating point number of (1); the 'update stop ratio' beta is taken as (0, 1)]The floating point number of (1); the 'parameter high risk ratio' gamma is (0, 1)]The floating point number of (1); "Risk-free number of iterations" El *Value is [0, E ]l]An integer of (d); "number of cache intervals" δ, taking the value of [1, El-El *]Is an integer of (1).
The dynamic fusion process of the neural network is as follows:
1) the method comprises the following steps that a task node constructs a neural network model f of a task T according to a network structure designed by a user, and initializes a global neural network f (w), wherein w is a parameter of the global neural network; initializing all computing nodes as low-risk computing nodes;
2) the task node combines the neural network model f, the hyper-parameters, the training configuration and the E of the task Tl *Delta sending to m screening centers1，…，Cm；
3) The task node copies the global neural network parameter w of the task T to C1，…，CmAs a local neural network f (w) for each screening center1)，…，f(wm) When w is equal to w1＝…＝wm；
4) Based on training configuration information, C1，…，CmThe low-risk computing nodes in the system respectively use local data D of the respective screening centers1，…，DmBegin training the local neural network f (w)1)，…，f(wm) Wherein w is1，…，wmFor local neural network parameters, w is not equal to w1≠…≠wm；
5) Local neural network f (w) at the ith screening centeri) Is reached by the number of iterations j
Figure BDA0002992577520000021
Then, the computing node C of the screening centeriSaving the local neural network parameters at the moment as optimal parameters
Figure BDA0002992577520000022
Local neural network f (w) at the ith screening centeri) Is satisfied with
Figure BDA0002992577520000023
And is
Figure BDA0002992577520000024
Then, the computing node C of the screening centeriSaving the local neural network parameter w at this timeijThe number of iterations E at this time is savediJ; and pause f (w)i) The iteration of (2) begins to send a model fusion request signal to the task node, at which time the compute node CiOther tasks may be performed;
6) when the task node receives model fusion request signals of all the computing nodes, alpha- | w | parameters are randomly selected from all | w | parameters of a global neural network f (w), index numbers corresponding to the parameters are recorded, and then the index numbers are sent to all the computing nodes;
7) when computing node CiAfter receiving the parameter index number sent by the task node, adding the task T into the calculation task queue again;
8) when computing node CiWhen the task queue of (2) executes to task T, CiReading local neural network parameters w in storageijSequentially fetching w according to the order of parameter index numbersijCorresponding parameters in the vector form a column vector
Figure BDA0002992577520000031
And sending to the task node;
9) after the task nodes receive the parameter vectors of all the computing nodes, a parameter matrix is formed
Figure BDA0002992577520000032
Calculating the upper quartile Q of the parameter matrix by row3Lower quartile Q1And four-bit distance IQR ═ Q3-Q1And a high risk cutoff value R1＝Q3+1.5IQR and R2＝Q1-1.5IQR；
10) The task node starts to count the risk proportion of each computing node, and one computing node CiRisk ratio gamma ofiThe calculation is as follows:
Figure BDA0002992577520000033
wherein
Figure BDA0002992577520000034
And
Figure BDA0002992577520000035
respectively represent
Figure BDA0002992577520000036
Middle greater than cutoff value R1And less than the cutoff value R2The number of parameters of (2);
11) if a computing node CiRisk ratio gamma ofiGreater than or equal to gamma, the task node sends CiMarking as high risk if one compute node CiRisk ratio gamma ofiLess than gamma, task node is Ci(ii) is flagged as low risk;
12) if the number of the computing nodes marked as high risk is more than beta m, the task node informs all the computing nodes to upload the optimal parameters
Figure BDA0002992577520000037
The global neural network will update the parameters in the following way;
Figure BDA0002992577520000038
Figure BDA0002992577520000039
wherein:
Figure BDA0002992577520000041
Figure BDA0002992577520000042
therein withoutiI and I represent the data volume of the ith screening center and the m screening centers; jumping to step 16) after the task node updates the complete local neural network parameter w);
13) if the number of the computing nodes marked as high risk is less than beta m, the task node sends a signal for stopping iteration to all high risk computing nodes and sends a signal for continuing iteration to all low risk nodes;
14) with CiFor example, when the high-risk computing node receives the signal for stopping iteration, the parameter w stored in the node is deletedijReleasing the resources of the task T for the execution of other tasks;
15) with CiFor example, when the low-risk computing node receives the signal of continuing iteration, the optimal parameter on the node is determined
Figure BDA0002992577520000043
Updated to parameter wijDeleting w stored on a nodeijThen continue the local neural network f (w)i) And jumps to step 4);
16) when the global iteration number reaches EgThen, stopping the dynamic fusion process of the neural network to obtain a global neural network; otherwise, the task node marks all the computing nodes as low-risk, informs all the computing nodes to empty storage and temporary variables generated by the global iteration, and jumps to step 3).
The invention has the beneficial effects that:
the invention designs a neural network dynamic fusion method for genetic metabolic disease multi-center screening, provides a parameter detection technology, can dynamically evaluate the fitting state and the synchronization degree of each central local model, reduces the times of model fusion, reduces the pressure of model parameter and gradient information exchange on communication load, and improves the efficiency of combined modeling.
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FIG. 1 is a schematic of the process of the present invention;
Detailed Description
The invention is further described with reference to the following figures and specific examples.
The invention relates to a neural network dynamic fusion method for genetic metabolic disease multi-center screening, which is a genetic metabolic disease multi-center screening scene, and two types of nodes are required to be equipped besides a plurality of screening centers: the system comprises task nodes and computing nodes, wherein the task nodes are responsible for management, distribution and maintenance of a multi-center screening task, a user can directly interact with the task nodes to perform operations such as task initiation, model construction and flow arrangement, the task nodes can be built on a third-party cloud platform independently of a screening center and can also be built by a regional medical center (the task nodes in the example are built based on the national regional medical center); each screening center needs to be provided with a computing node and is responsible for the task node to issue the computation of the joint modeling task, the computing node only interacts with the task node, and a user cannot directly access the computing node. The connection mode of the task node and the computing node can be any mainstream network topology structure (the connection mode of the example is a star topology structure).
When a user initiates a genetic metabolic disease multi-center screening task T, m screening centers participating in combined modeling are required to be selected; designing the structure and hyper-parameters of a neural network model, and model training configuration including the number of iterations E of the global modelgNumber of iterations of local model ElEtc.; configuring parameters of the dynamic fusion method: the parameter detection ratio alpha is set to be 0, 1]The floating point number of (1); the 'update stop ratio' beta is taken as (0, 1)]The floating point number of (1); the 'parameter high risk ratio' gamma is (0, 1)]The floating point number of (1); "Risk-free number of iterations"
Figure BDA0002992577520000051
Take the value of [0, El]An integer of (d); the number of times of cache interval delta is taken as
Figure BDA0002992577520000052
Is an integer of (1).
As shown in fig. 1, the dynamic fusion process of the neural network in the method of the present invention is as follows:
1) the method comprises the following steps that a task node constructs a neural network model f of a task T according to a network structure designed by a user, and initializes a global neural network f (w), wherein w is a parameter of the global neural network; initializing all computing nodes as low-risk computing nodes;
2) the task node calculates a neural network model f, a hyper-parameter, a training configuration and a,
Figure BDA0002992577520000053
Delta and other information are sent to computing nodes C of m screening centers1，…，Cm；
3) The task node copies the global neural network parameter w of the task T to C1，…，CmAs a local neural network f (w) for each screening center1)，…，f(wm) When w is equal to w1＝…＝wm；
4) Based on training configuration information, C1，…，CmThe low-risk computing nodes in the system respectively use local data D of the respective screening centers1，…，DmBegin training the local neural network f (w)1)，…，f(wm) Wherein w is1，…，wmFor local neural network parameters, w is not equal to w1≠…≠wm；
5) Local neural network f (w) at the ith screening centeri) Is reached by the number of iterations j
Figure BDA0002992577520000054
Then, the computing node C of the screening centeriSaving the local neural network parameters at the moment as optimal parameters
Figure BDA0002992577520000055
Local neural network f (w) at the ith screening centeri) Is satisfied with
Figure BDA0002992577520000056
And is
Figure BDA0002992577520000057
Then, the computing node C of the screening centeriSaving the local neural network parameter w at this timeijThe number of iterations E at this time is savediJ; and pause f (w)i) The iteration of (2) begins to send a model fusion request signal to the task node, at which time the compute node CiOther tasks may be performed;
6) when the task node receives model fusion request signals of all the computing nodes, alpha- | w | parameters are randomly selected from all | w | parameters of a global neural network f (w), index numbers corresponding to the parameters are recorded, and then the index numbers are sent to all the computing nodes;
7) when computing node CiAfter receiving the parameter index number sent by the task node, adding the task T into the calculation task queue again;
8) when computing node CiWhen the task queue of (2) executes to task T, CiReading local neural network parameters w in storageijSequentially fetching w according to the order of parameter index numbersijCorresponding parameters in the vector form a column vector
Figure BDA0002992577520000061
And sending to the task node;
9) after the task nodes receive the parameter vectors of all the computing nodes, a parameter matrix is formed
Figure BDA0002992577520000062
Calculating the upper quartile Q of the parameter matrix by row3Lower quartile Q1And four-bit distance IQR ═ Q3-Q1And a high risk cutoff value R1＝Q3+15IQR and R2＝Q1-1.5IQR；
10) The task node starts to count the risk proportion of each computing node, and one computing node CiRisk ratio gamma ofiThe calculation is as follows:
Figure BDA0002992577520000063
wherein
Figure BDA0002992577520000064
And
Figure BDA0002992577520000065
respectively represent
Figure BDA0002992577520000066
Middle greater than cutoff value R1And less than the cutoff value R2The number of parameters of (2);
11) if a computing node CiRisk ratio gamma ofiGreater than or equal to gamma, the task node sends CiMarking as high risk if one compute node CiRisk ratio gamma ofiLess than gamma, task node is Ci(ii) is flagged as low risk;
12) if the number of the computing nodes marked as high risk is more than beta m, the task node informs all the computing nodes to upload the optimal parameters
Figure BDA0002992577520000067
The global neural network will update the parameters in the following way;
Figure BDA0002992577520000068
Figure BDA0002992577520000069
wherein:
Figure BDA00029925775200000610
Figure BDA00029925775200000611
wherein | Di| and | D | represent the data volume of the ith screening center and the m screening centers; jumping to step 16) after the task node updates the complete local neural network parameter w);
13) if the number of the computing nodes marked as high risk is less than beta m, the task node sends a signal for stopping iteration to all high risk computing nodes and sends a signal for continuing iteration to all low risk nodes;
14) with CiFor example, when the high-risk computing node receives the signal for stopping iteration, the parameter w stored in the node is deletedijReleasing the resources of the task T for the execution of other tasks;
15) with CiFor example, when the low-risk computing node receives the signal of continuing iteration, the optimal parameter on the node is determined
Figure BDA0002992577520000071
Updated to parameter wijDeleting w stored on a nodeijThen continue the local neural network f (w)i) And jumps to step 4);
16) when the global iteration number reaches EgThen, stopping the dynamic fusion process of the neural network to obtain a global neural network; otherwise, the task node marks all the computing nodes as low-risk, informs all the computing nodes to empty storage and temporary variables generated by the global iteration, and jumps to step 3).

Claims (3)

1. A neural network dynamic fusion method for genetic metabolic disease multi-center screening is characterized in that the method is a genetic metabolic disease multi-center screening-oriented scene, and two types of nodes are required to be equipped besides a plurality of screening centers: the system comprises task nodes and computing nodes, wherein the task nodes are responsible for management, distribution and maintenance of a multi-center screening task, and a user directly interacts with the task nodes; each screening center needs to be provided with a computing node and is responsible for the task node to issue the computation of the joint modeling task, the computing node only interacts with the task node, and a user cannot directly access the computing node; the connection mode of the task node and the computing node is any mainstream network topology structure.
2. The dynamic neural network fusion method for genetic metabolic disease multi-center screening as claimed in claim 1, wherein when a user initiates a genetic metabolic disease multi-center screening task T, m screening centers participating in joint modeling need to be selected; designing the structure and hyper-parameters of a neural network model, and model training configuration including the number of iterations E of the global modelgNumber of iterations of local model El(ii) a Configuring parameters of the dynamic fusion method: the parameter detection ratio alpha is set to be 0, 1]The floating point number of (1); the 'update stop ratio' beta is taken as (0, 1)]The floating point number of (1); the 'parameter high risk ratio' gamma is (0, 1)]The floating point number of (1); "Risk-free number of iterations"
Figure FDA0002992577510000011
Take the value of [0, El]An integer of (d); the number of times of cache interval delta is taken as
Figure FDA0002992577510000012
Is an integer of (1).
3. The dynamic neural network fusion method for genetic metabolic disease multi-center screening according to claim 2, wherein the dynamic neural network fusion is performed as follows:
1) the method comprises the following steps that a task node constructs a neural network model f of a task T according to a network structure designed by a user, and initializes a global neural network f (w), wherein w is a parameter of the global neural network; initializing all computing nodes as low-risk computing nodes;
2) the task node calculates a neural network model f, a hyper-parameter, a training configuration and a,
Figure FDA0002992577510000013
Computing node C for sending delta to m screening centers1，…，Cm；
3) The task node copies the global neural network parameter w of the task T to C1，…，CmAs a local neural network f (w) for each screening center1)，…，f(wm) When w is equal to w1＝…＝wm；
4) Based on training configuration information, C1，…，CmThe low-risk computing nodes in the system respectively use local data D of the respective screening centers1，…，DmBegin training the local neural network f (w)1)，...，f(wm) Wherein w is1，…，wmFor local neural network parameters, w is not equal to w1≠…≠wm；
5) Local neural network f (w) at the ith screening centeri) Is reached by the number of iterations j
Figure FDA0002992577510000014
Then, the computing node C of the screening centeriSaving the local neural network parameters at the moment as optimal parameters
Figure FDA0002992577510000015
Local neural network f (w) at the ith screening centeri) Is satisfied with
Figure FDA0002992577510000021
And is
Figure FDA0002992577510000022
Then, the computing node C of the screening centeriSaving the local neural network parameter w at this timeijThe number of iterations E at this time is savedi＝j；And pause f (w)i) The iteration of (2) begins to send a model fusion request signal to the task node, at which time the compute node CiOther tasks may be performed;
6) when the task node receives model fusion request signals of all the computing nodes, alpha- | w | parameters are randomly selected from all | w | parameters of a global neural network f (w), index numbers corresponding to the parameters are recorded, and then the index numbers are sent to all the computing nodes;
7) when computing node CiAfter receiving the parameter index number sent by the task node, adding the task T into the calculation task queue again;
8) when computing node CiWhen the task queue of (2) executes to task T, CiReading local neural network parameters w in storageijSequentially fetching w according to the order of parameter index numbersijCorresponding parameters in the vector form a column vector
Figure FDA0002992577510000023
And sending to the task node;
9) after the task nodes receive the parameter vectors of all the computing nodes, a parameter matrix is formed
Figure FDA0002992577510000024
Calculating the upper quartile Q of the parameter matrix by row3Lower quartile Q1And four-bit distance IQR ═ Q3-Q1And a high risk cutoff value R1＝Q3+1.5IQR and R2＝Q1-1.5IQR；
10) The task node starts to count the risk proportion of each computing node, and one computing node CiRisk ratio gamma ofiThe calculation is as follows:
Figure FDA0002992577510000025
wherein
Figure FDA0002992577510000026
And
Figure FDA0002992577510000027
respectively represent
Figure FDA0002992577510000028
Middle greater than cutoff value R1And less than the cutoff value R2The number of parameters of (2);
11) if a computing node CiRisk ratio gamma ofiGreater than or equal to gamma, the task node sends CiMarking as high risk if one compute node CiRisk ratio gamma ofiLess than gamma, task node is Ci(ii) is flagged as low risk;
12) if the number of the computing nodes marked as high risk is more than beta m, the task node informs all the computing nodes to upload the optimal parameters
Figure FDA0002992577510000029
The global neural network will update the parameters in the following way;
Figure FDA00029925775100000210
Figure FDA0002992577510000031
wherein:
Figure FDA0002992577510000032
Figure FDA0002992577510000033
wherein | Di| and | D | represent the ith screeningData volume for heart and m screening centers; jumping to step 16) after the task node updates the complete local neural network parameter w);
13) if the number of the computing nodes marked as high risk is less than beta m, the task node sends a signal for stopping iteration to all high risk computing nodes and sends a signal for continuing iteration to all low risk nodes;
14) with CiFor example, when the high-risk computing node receives the signal for stopping iteration, the parameter w stored in the node is deletedijReleasing the resources of the task T for the execution of other tasks;
15) with CiFor example, when the low-risk computing node receives the signal of continuing iteration, the optimal parameter on the node is determined
Figure FDA0002992577510000034
Updated to parameter wijDeleting w stored on a nodeijThen continue the local neural network f (w)i) And jumps to step 4);
16) when the global iteration number reaches EgThen, stopping the dynamic fusion process of the neural network to obtain a global neural network; otherwise, the task node marks all the computing nodes as low-risk, informs all the computing nodes to empty storage and temporary variables generated by the global iteration, and jumps to step 3).
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007141325A1 (en) * 2006-06-09 2007-12-13 Bracco Spa Method of processing multichannel and multivariate signals and method of classifying sources of multichannel and multivariate signals operating according to such processing method
CN110473634A (en) * 2019-04-23 2019-11-19 浙江大学 A kind of Inherited Metabolic Disorders auxiliary screening method based on multiple domain fusion study
WO2020020088A1 (en) * 2018-07-23 2020-01-30 第四范式（北京）技术有限公司 Neural network model training method and system, and prediction method and system
CN111813858A (en) * 2020-07-10 2020-10-23 电子科技大学 Distributed neural network hybrid synchronous training method based on self-organizing grouping of computing nodes
CN112151192A (en) * 2020-10-22 2020-12-29 浙江大学 Genetic metabolic disease screening method based on implicit space reprojection

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007141325A1 (en) * 2006-06-09 2007-12-13 Bracco Spa Method of processing multichannel and multivariate signals and method of classifying sources of multichannel and multivariate signals operating according to such processing method
WO2020020088A1 (en) * 2018-07-23 2020-01-30 第四范式（北京）技术有限公司 Neural network model training method and system, and prediction method and system
CN110473634A (en) * 2019-04-23 2019-11-19 浙江大学 A kind of Inherited Metabolic Disorders auxiliary screening method based on multiple domain fusion study
CN111813858A (en) * 2020-07-10 2020-10-23 电子科技大学 Distributed neural network hybrid synchronous training method based on self-organizing grouping of computing nodes
CN112151192A (en) * 2020-10-22 2020-12-29 浙江大学 Genetic metabolic disease screening method based on implicit space reprojection

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
陈检;肖思隽;孙秋梅;: "基于迁移学习算法的糖网病自动筛选系统研究", 信息技术与信息化, no. 07 *

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