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WO2023063845A1 - Système et procédé d'apprentissage machine automatique (automl) de modèles de vision informatique pour l'analyse d'images biomédicales - Google Patents

Système et procédé d'apprentissage machine automatique (automl) de modèles de vision informatique pour l'analyse d'images biomédicales Download PDF

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Publication number
WO2023063845A1
WO2023063845A1 PCT/RU2021/000440 RU2021000440W WO2023063845A1 WO 2023063845 A1 WO2023063845 A1 WO 2023063845A1 RU 2021000440 W RU2021000440 W RU 2021000440W WO 2023063845 A1 WO2023063845 A1 WO 2023063845A1
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training
computer vision
automatically
image data
models
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Russian (ru)
Inventor
Сергей Юрьевич СОРОКИН
Иван Сергеевич ДРОКИН
Олег Леонидович БУХВАЛОВ
Елена Витальевна ЕРИЧЕВА
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Obshchestvo S Ogranichennoj Otvetstvennostyu "intellodzhik"
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Obshchestvo S Ogranichennoj Otvetstvennostyu "intellodzhik"
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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

Definitions

  • the invention relates to the field of information and communication technologies for processing medical data, in particular, to a system and method for automatic machine learning (AutoML) of computer vision models for analyzing biomedical images.
  • AutoML automatic machine learning
  • the presented solution can be used in medical decision support systems (DMSS), by doctors, for example, CT diagnostic doctors, MRI doctors, radiologists, radiologists, mammologists, oncologists and other specialists who analyze biomedical images obtained using various diagnostic methods.
  • doctors for example, CT diagnostic doctors, MRI doctors, radiologists, radiologists, mammologists, oncologists and other specialists who analyze biomedical images obtained using various diagnostic methods.
  • CT scans e.g. CT scans, MPT scans, ultrasound scans, x-rays, mammography, etc.
  • Patent US10282835B2 publication date 05/07/2019, describes a method and system for automatic analysis of clinical images using models developed using machine learning.
  • the system includes a server with an electronic processor and an interface for communication with the data source.
  • the electronic processor is configured to receive training information from a data source via an interface.
  • the training information includes a plurality of images and graphic reports associated with each of the plurality of images.
  • Each graphical report includes a graphic marker denoting a portion of one of the plurality of images and diagnostic information associated with a portion of one of the plurality of images.
  • the electronic processor is also configured to perform machine learning to develop a model using the training information.
  • the electronic processor is also configured to receive images for analysis and automatic image processing using a model to generate a diagnosis for the image.
  • the method includes: receiving the user's target and the first data set by the AutoML system; determining, according to the target, that the original artificial intelligence (AI) model is used to implement the user's target; training the AutoML system, according to the obtained first data set, the initial AI model to obtain the trained AI model; further analyzing, according to the first data set, the training of the initial AI model to obtain an analysis result, the analysis result including the effect of at least one type of data in the first data set on the training of the initial AI model.
  • AI artificial intelligence
  • An AutoML system is also described, providing, depending on the analysis result and the user, an optimization mode for the trained AI model, while the optimization mode can load a second data set to optimize the trained AL model.
  • the technical problem to be solved by the claimed invention is the development of methods and systems for automatic training of computer vision models for tasks related to biomedical images, the development of automated methods for evaluating and validating trained models, the development of a data and markup management system to provide the AutoML process, increasing accuracy of AutoML machine learning models for biomedical image analysis.
  • the technical result of the claimed invention is to expand the arsenal of technical tools for automating the creation of models machine learning for biomedical image analysis (e.g. CT scans, MPT scans, ultrasound scans, x-rays, mammography, angiography, and others), improving the accuracy of biomedical image analysis by choosing the best model, reducing biomedical image analysis time by automating search, training and evaluation of computer vision models, increasing the speed of processing a large number of biomedical images simultaneously with increasing accuracy, increasing the ability to adapt computer vision models to new cases, devices, research modes, etc.
  • biomedical image analysis e.g. CT scans, MPT scans, ultrasound scans, x-rays, mammography, angiography, and others
  • a computer-implemented automatic machine learning (AutoML) system for computer vision models for biomedical image analysis comprises: a database, the database storing biomedical image data; moreover, the data on the basis of which the biomedical image data is obtained is collected automatically; server containing:
  • the loader automatically loads the biomedical image data required for testing, training and validating computer vision models from the database;
  • transformation unit automatically transforms the biomedical image data received from the download unit into a format accepted by the search, learning and evaluation units;
  • search unit and with the help of the search unit, computer vision models are automatically searched using training and test samples generated on the basis of biomedical image data received from the transformation unit, and the architecture parameters of the found models are automatically searched and optimized;
  • the training unit automatically trains the computer vision models found by the search unit using a training sample generated on the basis of biomedical image data received from the transformation unit and using architecture parameters received from the search unit; wherein the best of said trained models is automatically selected and the selected model is passed to the estimator;
  • the estimator automatically evaluates the best selected computer vision model trained by the training unit using a validation set formed on the basis of biomedical image data received from the transformation unit.
  • data collection can be automatically carried out using a clinic agent, on the basis of which biomedical image data is obtained.
  • a model can be searched until the specified metric values are reached or until the search budget is exhausted.
  • additional training of the computer vision model found by the search unit can be carried out using a training sample, which is supplemented with data from additional biomedical images received from the transformation unit, if the specified model has not passed validation.
  • the training and evaluation units may be configured to initiate a repeated search and training process for computer vision models for biomedical image analysis.
  • biomedical image data required for testing, training and validation of computer models vision; moreover, the data on the basis of which the biomedical image data is obtained is collected automatically;
  • loading of biomedical image data can be automatically performed using a loading block
  • automatic collection of data from which biomedical image data is obtained can be performed using a clinic agent
  • transformation of the loaded biomedical image data can be automatically performed using a transformation block
  • the computer vision models can be automatically searched with the search block
  • the found computer vision models can be automatically trained with the training block
  • the best selected trained computer vision model can be automatically evaluated with the evaluation block.
  • the method can be used to search for a model until the specified values of the metrics are reached or until the search budget is exhausted.
  • additional training of the computer vision model found by the search unit can be carried out using a training set, in which additional biomedical image data received from the transformation unit is added, if the specified model has not passed the validation.
  • the method may further initiate a repeated process of searching and training computer vision models for biomedical image analysis.
  • Fig. 1 illustrates an example of a general architecture of which an automatic machine learning (AutoML) system of computer vision models for biomedical image analysis is a part.
  • AutoML automatic machine learning
  • Fig. 2 - illustrates the general scheme for constructing AutoML computer vision models for biomedical image analysis using hybrid intelligence.
  • Fig. 3 - illustrates the general scheme of the learning agent device.
  • Fig. 4 illustrates the general scheme for updating AutoML computer vision models for biomedical image analysis.
  • Fig. 5 - illustrates the general scheme of the device of the clinic agent.
  • Fig. 6 illustrates a general diagram of a computing device for implementing the present invention.
  • the present invention discloses an automatic machine learning (AutoML) system for computer vision models for analyzing biomedical images.
  • the system is designed to automate the stages of development and training of computer vision models in the tasks of biomedical image analysis.
  • Biomedical images are medical images obtained by various methods, for example, methods of radiation diagnostics (X-ray, magnetic resonance, radionuclide, ultrasound, etc.) - computed tomography (CT) images, magnetic resonance imaging (MRI) images, ultrasound images ( ultrasonography), positron emission tomography (PET) images, x-rays, mammography, angiography images, elastography images, etc.), through an endoscope (endoscopic images), using photographic methods (for example, medical photographs of skin conditions and other superficial conditions, such as the palate, birthmarks, moles, etc.), etc.
  • CT computed tomography
  • MRI magnetic resonance imaging
  • PET positron emission tomography
  • x-rays x-rays
  • mammography mammography
  • the task of building machine learning models consists of the following steps:
  • Data preparation normalization, cleaning, search for outliers.
  • the Clinic Agent provides automation of data collection from clinics based on a system of rules and filters.
  • the clinic agent is also responsible for technical integration and data download, validation and storage.
  • the work of a clinic agent is based on sets of rules, filters and lists of DICOM tags. Based on this data, it is possible to automate the processes of access, technical integration, validation, download, storage and retrieval of biomedical image data.
  • Data collection is carried out from internal sources (for example, mini and postgre databases storing biomedical images) by automatic copying to the point of work - to the server where the training model will be launched.
  • the training agent is responsible for dividing the prepared data into test, validation and training sets, choosing the model architecture, choosing hyperparameters, training the selected model, and evaluating the model, which collects all actions into a chain of tasks that are performed on computing resources in sequential mode.
  • Automatic sampling relies on industry-leading AutoML approaches based on parsing the markup in the data to partition the samples in a stratified manner.
  • model architecture is based on the methods of Neural Architecture Serach (NAS) - a branch of machine learning that solves the problem of finding the best model in the context of a training set.
  • NAS Neural Architecture Serach
  • a method is used based on the adaptation of NAS methods to the specifics of medical data - small sample sizes, the task of segmenting biomedical images as a key task of analysis, the use of existing solutions as a starting point for searching for computer vision models for analyzing biomedical images.
  • the found architecture is also trained in automatic mode, which eliminates the need for manual launches and selection of training parameters, which reduces human participation in this cycle.
  • Model evaluation is performed on the basis of a prepared protocol, which allows you to evaluate all the necessary model metrics in automatic mode.
  • FIG. 1 shows an example of a general architecture, of which an automatic machine learning (AutoML) system for computer vision models for biomedical image analysis is a part.
  • AutoML automatic machine learning
  • Botkin Main Platform the main platform, the central cloud of the Botkin.AI ecosystem. Carries out the relationship between all agents and subsystems, including managing data flows used for training and labeling models.
  • groups of services 1.
  • Agent Manager infrastructure management services that perform the following functions:
  • Model Registry model artifacts
  • Process Schedule Management - process planning service This service performs the following functions:
  • Platform Controller - a service for coordinating system processes.
  • Botkin Secondary Platform - secondary secondary platform Botkin.AL It differs from the main platform in that there are no AutoML management services, and process scheduling tasks are delegated to the main platform.
  • Inference Agent is an inference agent whose task is to process medical images using already trained models.
  • the Learning Agent a learning agent whose task is to find and train new machine learning models.
  • the learning agent contains several subcomponents: a module for interacting with the system, a module for training computer vision models, a module for automatically deploying a model in industrial outline. This module is deployed on servers with sufficient computing resources. Multiple copies may be deployed.
  • Clinic Agent is a clinic agent that is deployed on the side of the clinic and provides a means of interaction with the clinic's information systems.
  • Satellite - agent management service
  • ML Service - a service that performs the processing of studies by a computer vision model.
  • Report Service - a service that generates reports in the DICOM standard based on the results of processing a series of studies by a machine learning model.
  • Learning Service is a service that trains machine learning models, including machine learning algorithms.
  • Cloud Provider - provider of cloud servers.
  • PACS English Picture Archiving and Communication System
  • Botkin Resource Layer resource management layer.
  • the 3rd Party DICOM Viewer is a doctor-supplied viewer, such as a web viewer or a standalone viewer, that contains all the necessary tools for biomedical image analysis, labeling biomedical images according to required protocols, and interacts with the system in terms of data addressing and tasks.
  • FIG. Figure 2 shows a general scheme for building computer vision models for analyzing biomedical images based on two key technologies - AutoML technology, which automates the routine work of computer vision specialists, and hybrid intelligence - a group of methods that allow taking into account feedback from a person (for example, a radiologist) and using it to update AutoML models.
  • the stages where AutoML and hybrid intelligence are used are highlighted in color.
  • the physician(s) mark up a pool of biomedical image data.
  • the data is uploaded to the server for training.
  • the AutoML algorithm is launched, which consists of the following steps: data preparation, search for suitable model architectures, training of selected architectures, selection of the best model, testing on a delayed sample.
  • the model is updated in the industrial loop, otherwise this step is skipped.
  • the data is processed by the current version of the model and provided to the doctor for validation. If the validation result is unsatisfactory (FAIL), the data is returned to the markup and the process is repeated.
  • FAIL unsatisfactory
  • FIG. Figure 3 shows the general layout of the learning agent device.
  • Satellite Service designed to train artificial intelligence models for biomedical image analysis tasks.
  • FIG. 3 shows the following learning agent services:
  • Satellite - managing agent service 1. Satellite - managing agent service.
  • the service consists of the following components:
  • Data Preprocessor data preparation module - a block that performs the transformation of data received from the data loading block into a format accepted by the blocks for searching for models and their training;
  • Model Search block a block that implements a set of AutoML methods for searching and optimizing metaparameters. Starts and controls the model search process;
  • Block - a block that trains the model according to the found architecture parameters. If necessary, may initiate a second learning search process; (In case of incorrect completion of training or problems of an infrastructural nature (temporary communication problems, equipment reboot, etc.).
  • Model Test block - a block that performs testing and evaluation of model metrics on a delayed sample. If necessary, it can initiate a repeated learning search process, for example, if the specified metric values are not reached on the test sample.
  • FIG. 4 shows a general scheme for updating AutoML computer vision models for biomedical image analysis.
  • FIG. 5 shows a general diagram of the device of the clinic agent.
  • Clinic agents are a group of services managed by the Satellite service, deployed on the side of the clinic, designed to be integrated with the clinic's information systems, devices, radiologists' tools, etc.
  • the clinic agent periodically, for example, once a day at midnight, selects all studies that have entered the clinic's PACS in the last 24 hours.
  • the clinic agent sends the collected biomedical image data to the main or auxiliary platform for processing, and returns the results of the biomedical image analysis to the responsible doctor.
  • AutoML automatic machine learning
  • the learning agent loads from the storage locations specified in the configuration file the mammography data as images and the generated annotations for the specified images.
  • Annotation is created by physicians and usually consists of a class of study (normal or pathological, such as breast cancer) and a set of regions of interest associated with the mammographic image.
  • the configuration file specifies the necessary parameters for the operation of the learning agent, for example, the search budget (how many hours of computing resources can be spent on searching), the type of problem being solved (classification, segmentation), service information (for example, addresses of alloying servers), the share of training and test examples in the sample, image parameters in the study (their number) and the number of channels (classes) into which the samples are divided, etc.
  • the learning agent processes the received data (for example, for raw data from the DICOM viewports embedded in the file, determines the projections of the image) and saves the data in the accepted format on the server (for example, in the form of binary files containing 4 images (images of each breast in two projections, and images of regions of interest.)
  • the learning agent launches methods for preparing data partitioning into training and test data. For example, a stratified partition by the presence of a norm and a pathology into two samples according to specified proportions. One patient can enter only one sample - training or a test one, even if it has more than one study.
  • the learning agent runs the learning methods that represent is a variation of a method called Neural Architecture Search (NAS) based on a gradient architecture search.
  • NAS Neural Architecture Search
  • a basic architecture consisting of large blocks (for example, Linet) is used. Each block is searched by optimizing links between nodes.
  • the search process is a search for such a set of weights that achieves a minimum of training error.
  • the final architecture itself is obtained by binarizing (removing) links that have too low a weight.
  • unified models are used that differ only in parameters. Alloying takes place in the ML Flow service.
  • DS Data Science
  • a specialist has access to logs to evaluate the performance of the model.
  • the search for suitable models occurs until the specified values of the metrics are reached.
  • the search is carried out by running the learning method with different metaparameters (training step size, regularization parameters, data augmentation parameters, etc.).
  • the criterion for choosing models for the analysis of mammograms is, for example, maximizing the value of the AUC metric (area under the ROC-curve) to determine the norm / pathology for the study on the entire test sample.
  • the traditional threshold value AUC 0.85.
  • the validation set is created from a separate data source that is not represented in the test or training dataset, otherwise the process is similar to the process of creating training and test sets.
  • a workflow is launched that sends mammographic data from the validation dataset to the trained model, which performs processing, and as a result, annotated mammographic images generated by the model are obtained.
  • Mammography images processed by the trained model are assigned to a doctor who checks the quality of the model on the data provided. If the model fails validation, the decision is usually made to add training data and repeat the training process.
  • FIG. 6 shows a general diagram of a computing device (600) that provides the data processing necessary to implement the claimed solution.
  • the device (600) contains components such as: one or more processors (601), at least one memory (602), storage media (603), input/output interfaces (604), I/O ( 605), networking tools (606).
  • processors such as: one or more processors (601), at least one memory (602), storage media (603), input/output interfaces (604), I/O ( 605), networking tools (606).
  • the processor (601) of the device performs the basic computing operations necessary for the operation of the device (600) or the functionality of one or more of its components.
  • the processor (601) executes the necessary machine-readable instructions contained in the main memory (602).
  • the memory (602) is typically in the form of RAM and contains the necessary software logic to provide the desired functionality.
  • the data storage means (603) can be in the form of HDD, SSD disks, raid array, network storage, flash memory, optical information storage devices (CD, DVD, MD, Blue-Ray disks), etc.
  • the means (603) allows long-term storage of various types of information.
  • Interfaces (604) are standard means for connecting and working with the server part, for example, USB, RS232, RJ45, LPT, COM, HDMI, PS/2, Lightning, FireWire, etc.
  • interfaces (604) depends on the specific implementation of the device (N00), which can be a personal computer, mainframe, server cluster, thin client, smartphone, laptop, etc.
  • the data I/O means (605) in any embodiment of the system must be a keyboard.
  • the keyboard hardware can be any known: it can be either a built-in keyboard used on a laptop or netbook, or a separate device connected to a desktop computer, server, or other computer device.
  • the connection can be either wired, in which the keyboard connection cable is connected to the PS / 2 or USB port located on the system unit of the desktop computer, or wireless, in which the keyboard exchanges data via a wireless communication channel, for example, a radio channel, with base station, which, in turn, is directly connected to the system unit, for example, to one of the USB- ports.
  • the following I/O devices can also be used: joystick, display (touchscreen), projector, touchpad, mouse, trackball, light pen, speakers, microphone, etc.
  • Means of networking are selected from devices that provide network reception and transmission of data, for example, an Ethernet card, WLAN/Wi-Fi module, Bluetooth module, BLE module, NFC module, IrDa, RFID module, GSM modem, etc.
  • an Ethernet card for example, WAN, PAN, LAN (LAN), Intranet, Internet, WLAN, WMAN or GSM, 3G, 4G, 5G, is provided.
  • the components of the device (600) are coupled via a common data bus (607).

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Abstract

L'invention concerne un système et un procédé d'apprentissage machine automatique (AutoML) de modèles de vision informatique pour l'analyse d'images biomédicales. Ce système comprend une base de données d'images biomédicales, un serveur assurant: le chargement de données d'images biomédicales à des fins de test, d'instruction et de validation de modèles de vision informatique; la conversion de données d'images biomédicales en un format requis pour la recherche, l'instruction et l'évaluation; la recherche de modèles de vision informatique à l'aide de sélections d'instruction et de test; la recherche et l'optimisation de paramètres d'architectures des modèles trouvés; l'instruction de modèles de vision informatique à l'aide de la sélection d'instruction générée sur la base des données d'images biomédicales; le choix du meilleur modèle parmi ceux instruits; l'estimation du meilleur modèle choisi de vision informatique à l'aide de la sélection de validation générée sur la base des données d'images biomédicales.
PCT/RU2021/000440 2021-10-14 2021-10-18 Système et procédé d'apprentissage machine automatique (automl) de modèles de vision informatique pour l'analyse d'images biomédicales Ceased WO2023063845A1 (fr)

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RU2021129912A RU2787558C1 (ru) 2021-10-14 СИСТЕМА И СПОСОБ АВТОМАТИЧЕСКОГО МАШИННОГО ОБУЧЕНИЯ (AutoML) МОДЕЛЕЙ КОМПЬЮТЕРНОГО ЗРЕНИЯ ДЛЯ АНАЛИЗА БИОМЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160364527A1 (en) * 2015-06-12 2016-12-15 Merge Healthcare Incorporated Methods and Systems for Automatically Analyzing Clinical Images and Determining when Additional Imaging May Aid a Diagnosis
WO2021035412A1 (fr) * 2019-08-23 2021-03-04 华为技术有限公司 Système, procédé et dispositif d'apprentissage automatique autonome (automl)
US11094034B2 (en) * 2018-06-26 2021-08-17 International Business Machines Corporation Determining appropriate medical image processing pipeline based on machine learning

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160364527A1 (en) * 2015-06-12 2016-12-15 Merge Healthcare Incorporated Methods and Systems for Automatically Analyzing Clinical Images and Determining when Additional Imaging May Aid a Diagnosis
US10282835B2 (en) * 2015-06-12 2019-05-07 International Business Machines Corporation Methods and systems for automatically analyzing clinical images using models developed using machine learning based on graphical reporting
US11094034B2 (en) * 2018-06-26 2021-08-17 International Business Machines Corporation Determining appropriate medical image processing pipeline based on machine learning
WO2021035412A1 (fr) * 2019-08-23 2021-03-04 华为技术有限公司 Système, procédé et dispositif d'apprentissage automatique autonome (automl)

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
GESSERT NILS ET AL.: "Efficient Neural Architecture Search on Low-Dimensional Data for OCT Image Segmentation", MEDICAL IMAGING WITH DEEP LEARNING 2019 CONFERENCE, XP081273317 *
YAN JIANGCHENG, RUI SHI, BINGBING NI: "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis", IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, 28 October 2020 (2020-10-28), pages 1 - 5, XP093061894 *

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