WO2022077720A1 - Procédé et appareil de partage de données médicales - Google Patents

Procédé et appareil de partage de données médicales Download PDF

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Publication number
WO2022077720A1
WO2022077720A1 PCT/CN2020/132615 CN2020132615W WO2022077720A1 WO 2022077720 A1 WO2022077720 A1 WO 2022077720A1 CN 2020132615 W CN2020132615 W CN 2020132615W WO 2022077720 A1 WO2022077720 A1 WO 2022077720A1
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Prior art keywords
data
medical data
assistant
shadow
seeking
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PCT/CN2020/132615
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English (en)
Chinese (zh)
Inventor
赵建
罗凯文
相韶华
林宏壤
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深圳技术大学
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Publication of WO2022077720A1 publication Critical patent/WO2022077720A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/17—Details of further file system functions
    • G06F16/176—Support for shared access to files; File sharing support
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/20—ICT specially adapted for the handling or processing of medical images for handling medical images, e.g. DICOM, HL7 or PACS
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation

Definitions

  • the present invention relates to the technical field of data processing, and in particular, to a medical data sharing method and device.
  • Federated machine learning (Federated machine learning/Federated Learning): also known as federated learning, federated learning, and federated learning.
  • Federated Machine Learning is a machine learning framework that can effectively help multiple agencies conduct data usage and machine learning modeling while meeting user privacy protection, data security, and government regulations.
  • federated learning can effectively solve the problem of data silos, allowing participants to jointly model without sharing data, which can technically break the data silos and realize AI collaboration.
  • Federated learning defines a machine learning framework under which the problem of different data owners collaborating without exchanging data is solved by designing virtual models.
  • the virtual model is the optimal model for all parties to aggregate data together, and each region serves local goals according to the model.
  • Federated learning requires that this modeling result should be infinitely close to the traditional model, that is, the result of aggregating the data of multiple data owners into one place for modeling.
  • the identity and status of each participant are the same, and a shared data policy can be established. Since the data is not transferred, it does not reveal user privacy or affect data compliance. To protect data privacy and meet legal compliance requirements.
  • the main purpose of the present invention is to propose a medical data sharing method and device, which realizes decentralization through blockchain technology and federated machine learning training, and solves the shortcomings of traditional federated learning relying on a single server.
  • the main purpose of the present invention is to propose a medical data sharing method and device, which realizes decentralization through blockchain technology and federated machine learning training, and solves the shortcomings of traditional federated learning relying on a single server.
  • a medical data sharing method includes:
  • consortium chains to connect multiple data islands includes:
  • Synthetic training is performed by the temporary center generator to generate synthetic data samples that are similar to the data in the plurality of data islands.
  • the summary and update of the medical data of each node in the shadow-seeking assistant is specifically:
  • an intelligent ophthalmology OCT module is set in the shadow-seeking assistant, and the intelligent ophthalmology OCT module has OCT image quality control, AI lesion screening, AI lesion automatic detection, OCT image quality enhancement, AI-assisted labeling and multi-node.
  • OCT image quality control AI lesion screening, AI lesion automatic detection, OCT image quality enhancement, AI-assisted labeling and multi-node.
  • OCT image quality control AI lesion screening, AI lesion automatic detection, OCT image quality enhancement, AI-assisted labeling and multi-node.
  • the smart ophthalmology OCT module is mounted on a Zhilian platform.
  • a medical data sharing device comprising:
  • connection module is used to connect multiple data islands using the alliance chain
  • the publishing module is used to publish federated machine learning tasks in the form of smart contracts on the alliance chain;
  • the update module is used to summarize and update the medical data of each node in the shadow-seeking assistant.
  • connection module includes:
  • a setting unit for setting a one-to-one corresponding multiple distributed asynchronous discriminators in the multiple data islands
  • an adversarial unit configured to form an adversarial network of the plurality of distributed asynchronous discriminators and a temporary center generator
  • a training unit configured to perform synthetic training through the temporary center generator to generate synthetic data samples that are similar to the data in the plurality of data islands.
  • the summary and update of the medical data of each node in the shadow-seeking assistant is specifically:
  • an intelligent ophthalmology OCT module is set in the shadow-seeking assistant, and the intelligent ophthalmology OCT module has OCT image quality control, AI lesion screening, AI lesion automatic detection, OCT image quality enhancement, AI-assisted labeling and multi-node.
  • OCT image quality control AI lesion screening, AI lesion automatic detection, OCT image quality enhancement, AI-assisted labeling and multi-node.
  • OCT image quality control AI lesion screening, AI lesion automatic detection, OCT image quality enhancement, AI-assisted labeling and multi-node.
  • the smart ophthalmology OCT module is mounted on a Zhilian platform.
  • a medical data sharing method and device provided by the present invention include: using a consortium chain to connect multiple data islands; publishing a federated machine learning task in the form of a smart contract on the consortium chain;
  • the shadow assistant joins federated machine learning; it aggregates and updates the medical data of each node in the shadow assistant; through blockchain technology and federated machine learning training, it achieves decentralization and solves the shortcomings of traditional federated learning relying on a single server.
  • FIG. 1 is a flowchart of a medical data sharing method according to Embodiment 1 of the present invention
  • Fig. 2 is the method flow chart of step S10 in Fig. 1;
  • FIG. 3 is an exemplary structural block diagram of a medical data sharing apparatus according to Embodiment 2 of the present invention.
  • FIG. 4 is an exemplary structural block diagram of a connection module according to Embodiment 2 of the present invention.
  • a medical data sharing method includes:
  • the decentralization of medical data is realized through blockchain technology and federated machine learning training, which solves the disadvantage that traditional federated learning relies on a single server.
  • the smart ophthalmology OCT module in the shadow-seeking assistant is configured with a smart contract API interface, and nodes on the chain can join federated machine learning training through this interface, and the blockchain can continuously store each node in such an iterative process.
  • the update record of achieves decentralization, and solves the shortcomings of traditional federated learning relying on a single server.
  • the system selects temporary nodes at regular intervals to summarize and update the parameters, and feed the results back to the nodes and auxiliary systems.
  • the step S10 includes:
  • the Zhilian platform introduces federated machine learning to realize multi-party collaborative training, so that enterprises and institutions can collaborate on the shared model without sharing patient data to achieve decentralized Neural network training.
  • the Zhilian platform After acquiring the training task, the Zhilian platform forms an adversarial network by combining distributed asynchronous discriminators located in multiple data islands and a temporary central generator, so that the temporary central generator can also conduct training without touching the original private data. Synthetic training, so that synthetic data samples that are similar to the original data in each data island can be generated for downstream tasks. On this basis, two loss functions are also used, so that the temporary center generator has a certain lifelong learning ability, and can continue to learn in the dynamically changing data island environment. There are cases where institutions drop out and learn incrementally from different data silos to approximate the distribution of homogeneous data or even different data.
  • the Zhilian platform adheres to the core advantages of federated learning and solves the problem of privacy protection of medical data well. Not only that, compared with the traditional federated learning, the Zhilian platform also effectively reduces the amount of communication data between the temporary center generator and the data island due to the "different approach" in the implementation method, only the transmission of synthetic image data and feedback errors are not required. Not all parameter data of the entire model, and there is no need to exchange any data or parameters between data islands, which can significantly reduce the cost of research through federated learning between medical institutions, and speed up research efficiency and AI model production.
  • the summary and update of the medical data of each node in the shadow-seeking assistant is specifically:
  • each node adopts an algorithm suitable for nodes to update model parameters in a distributed manner, and completes the distributed update and convergence of machine learning model parameters with the support of a decentralized parameter server.
  • a node will be randomly selected by the smart contract as a temporary server, which is responsible for processing the gradient information of all nodes.
  • the smart contract will send the public key of the ephemeral server to other nodes participating in the training.
  • Each node will train the model locally, obtain gradient information, store the gradient information locally, and then encrypt the storage path with the public key, and write the encrypted storage path into the smart contract. Subsequently, the ephemeral server reads all the collected encrypted storage paths from the smart contract.
  • the temporary server After the temporary server reads the encrypted paths, it decrypts them, and then downloads the gradient information of this round from the corresponding nodes. After the temporary server obtains all the gradient information, it will integrate the gradient information. The simplest way to integrate is to average all the gradients. After integration, the temporary server stores the integrated parameters locally, encrypts the storage paths with the public keys of all nodes, and writes them into the smart contract. Each node obtains the parameter path encrypted by its own public key by reading the smart contract. After decrypting the parameter path, connect to the temporary server to download the parameter information updated in this round. Because the temporary server for each round of training is randomly selected by the smart contract, the temporary server for each round is almost different, so no node can obtain all the intermediate gradient information. However, each node only grasps the intermediate gradients of very few rounds, thus eliminating the risk of privacy leakage caused by intermediate gradients.
  • an intelligent ophthalmology OCT module is set in the shadow-seeking assistant, and the intelligent ophthalmology OCT module has the functions of OCT image quality control, AI lesion screening, AI lesion automatic detection, OCT image quality enhancement, AI auxiliary annotation and The function of multi-node collaborative training.
  • using the system can help the ophthalmologist to complete the preliminary fundus disease screening, and greatly save the patient's examination time.
  • Intelligent ophthalmology OCT combines OCT fundus examination and AI lesion screening.
  • the AI-assisted labeling system will also continue to learn during this process, and perform image processing and labeling, so that each new labelled image can become training data. Further Improve the accuracy of the model.
  • the smart ophthalmic OCT module is mounted on the Zhilian platform and Clara.
  • the Clara platform is a software toolkit launched by Nvidia that provides medical application developers with a series of libraries for GPUs, such as computing, advanced visualization, and AI technology-related libraries.
  • the smart ophthalmology oct system is also equipped with a smart contract API interface. Nodes on the chain can join federated machine learning training through this interface.
  • the blockchain can continuously store the update records of each node in such an iterative process, realizing decentralization. It solves the shortcomings of traditional federated learning relying on a single server.
  • the system selects temporary nodes at regular intervals to summarize and update the parameters, and feed the results back to the nodes and auxiliary systems.
  • a medical data sharing device includes:
  • connection module 10 is used to connect multiple data islands by using the alliance chain
  • the publishing module 20 is used to publish federated machine learning tasks in the form of smart contracts on the alliance chain;
  • the joining module 30 is used to add the shadow-seeking assistant to the federated machine learning through the smart contract API interface;
  • the updating module 40 is used for summarizing and updating the medical data of each node in the shadow-seeking assistant.
  • the decentralization of medical data is realized through blockchain technology and federated machine learning training, which solves the disadvantage that traditional federated learning relies on a single server.
  • the smart ophthalmology OCT module in the shadow-seeking assistant is configured with a smart contract API interface, and nodes on the chain can join federated machine learning training through this interface, and the blockchain can continuously store each node in such an iterative process.
  • the update record of achieves decentralization, and solves the shortcomings of traditional federated learning relying on a single server.
  • the system selects temporary nodes at regular intervals to summarize and update the parameters, and feed the results back to the nodes and auxiliary systems.
  • connection module includes:
  • a setting unit 11 is used to set a plurality of distributed asynchronous discriminators in one-to-one correspondence in the plurality of data islands;
  • an adversarial unit 12 configured to form an adversarial network with the multiple distributed asynchronous discriminators and a temporary center generator
  • the training unit 13 is configured to perform synthetic training through the temporary center generator to generate synthetic data samples that are similar to the data in the multiple data islands.
  • the Zhilian platform introduces federated machine learning to realize multi-party collaborative training, so that enterprises and institutions can collaborate on the shared model without sharing patient data to achieve decentralized Neural network training.
  • the Zhilian platform After acquiring the training task, the Zhilian platform forms an adversarial network by combining distributed asynchronous discriminators located in multiple data islands and a temporary central generator, so that the temporary central generator can also conduct training without touching the original private data. Synthetic training, so that synthetic data samples that are similar to the original data in each data island can be generated for downstream tasks. On this basis, two loss functions are also used, so that the temporary center generator has a certain lifelong learning ability, and can continue to learn in the dynamically changing data island environment. There are cases where institutions drop out and learn incrementally from different data silos to approximate the distribution of homogeneous data or even different data.
  • the Zhilian platform adheres to the core advantages of federated learning and solves the problem of privacy protection of medical data well. Not only that, compared with the traditional federated learning, the Zhilian platform also effectively reduces the amount of communication data between the temporary center generator and the data island due to the "different approach" in the implementation method, only the transmission of synthetic image data and feedback errors are not required. Not all parameter data of the entire model, and there is no need to exchange any data or parameters between data islands, which can significantly reduce the cost of research through federated learning between medical institutions, and speed up research efficiency and AI model production.
  • the summary and update of the medical data of each node in the shadow-seeking assistant is specifically:
  • an intelligent ophthalmology OCT module is set in the shadow-seeking assistant, and the intelligent ophthalmology OCT module has the functions of OCT image quality control, AI lesion screening, AI lesion automatic detection, OCT image quality enhancement, AI auxiliary annotation and The function of multi-node collaborative training.
  • each node adopts an algorithm suitable for nodes to update model parameters in a distributed manner, and completes the distributed update and convergence of machine learning model parameters with the support of a decentralized parameter server.
  • a node will be randomly selected by the smart contract as a temporary server, which is responsible for processing the gradient information of all nodes.
  • the smart contract will send the public key of the ephemeral server to other nodes participating in the training.
  • Each node will train the model locally, obtain gradient information, store the gradient information locally, and then encrypt the storage path with the public key, and write the encrypted storage path into the smart contract. Subsequently, the ephemeral server reads all the collected encrypted storage paths from the smart contract.
  • the temporary server After the temporary server reads the encrypted paths, it decrypts them, and then downloads the gradient information of this round from the corresponding nodes. After the temporary server obtains all the gradient information, it will integrate the gradient information. The simplest way to integrate is to average all the gradients. After integration, the temporary server stores the integrated parameters locally, encrypts the storage paths with the public keys of all nodes, and writes them into the smart contract. Each node obtains the parameter path encrypted by its own public key by reading the smart contract. After decrypting the parameter path, connect to the temporary server to download the parameter information updated in this round. Because the temporary server for each round of training is randomly selected by the smart contract, the temporary server for each round is almost different, so no node can obtain all the intermediate gradient information. However, each node only grasps the intermediate gradients of very few rounds, thus eliminating the risk of privacy leakage caused by intermediate gradients.
  • an intelligent ophthalmology OCT module is set in the shadow-seeking assistant, and the intelligent ophthalmology OCT module has the functions of OCT image quality control, AI lesion screening, AI lesion automatic detection, OCT image quality enhancement, AI auxiliary annotation and The function of multi-node collaborative training.
  • using the system can help the ophthalmologist to complete the preliminary fundus disease screening, and greatly save the patient's examination time.
  • Intelligent ophthalmology OCT combines OCT fundus examination and AI lesion screening.
  • the AI-assisted labeling system will also continue to learn during this process, and perform image processing and labeling, so that each new labelled image can become training data. Further Improve the accuracy of the model.
  • the smart ophthalmic OCT module is mounted on the Zhilian platform and Clara.
  • the Clara platform is a software toolkit launched by Nvidia that provides medical application developers with a series of libraries for GPUs, such as computing, advanced visualization, and AI technology-related libraries.
  • the smart ophthalmology oct system is also equipped with a smart contract API interface. Nodes on the chain can join federated machine learning training through this interface.
  • the blockchain can continuously store the update records of each node in such an iterative process, realizing decentralization. It solves the shortcomings of traditional federated learning relying on a single server.
  • the system selects temporary nodes at regular intervals to summarize and update the parameters, and feed the results back to the nodes and auxiliary systems.

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Abstract

La présente invention se rapporte au domaine technique du traitement de données. Sont divulgués un procédé et un appareil de partage de données médicales. Le procédé consiste : à utiliser une chaîne de consortium pour connecter une pluralité d'îlots de données ; à publier une tâche d'apprentissage machine fédéré sous la forme d'un contrat intelligent sur la chaîne de consortium ; à ajouter un assistant de recherche d'image à un apprentissage machine fédéré au moyen d'une interface API de contrat intelligent ; et à résumer et à mettre à jour des données médicales de chaque nœud dans l'assistant de recherche d'image. La décentralisation est obtenue au moyen d'une technologie de chaîne de blocs et d'un entraînement d'apprentissage machine fédéré, ce qui réduit l'inconvénient d'un apprentissage fédéré traditionnel reposant sur un serveur unique.
PCT/CN2020/132615 2020-10-15 2020-11-30 Procédé et appareil de partage de données médicales WO2022077720A1 (fr)

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CN202011104552.9A CN112162959B (zh) 2020-10-15 2020-10-15 一种医疗数据共享方法及装置

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