Artificial intelligence small sample meta-learning training method for medical image classification processing

Abstract

The invention discloses an artificial intelligence small sample meta-learning training method for medical image classification processing, and the method comprises the steps: building three meta-learners: a multi-scale CNN feature extractor, a metric learner and a classification discriminator, and designing a metric standard on the meta-learners; on each task of the training set, learning the target set through support set distance measurement, and obtaining a measurement standard through learning. For a new task of the test set, the target set can be quickly and correctly classified only by means of a small number of samples of the support set; the practicability and reliability of artificial intelligence automatic detection in the field of difficult diseases are improved by adopting meta-learning; the defect that a single disease type is small in data size and disease types are scattered is overcome, classification few-sample learning training of the medical image processing intelligent system is completed, the filtering accuracy is remarkably improved, the medical image classification accuracy is effectively improved, the production efficiency is greatly improved, and upgradingof the intelligent diagnosis deep learning technology in the medical industry is facilitated.

Description

technical field [0001] The present invention relates to the structural improvement technology of equipment and devices, belonging to the G06T7 / 11 machine learning, pattern recognition and medical image processing technology in the IPC classification, or G06K9 / 00 for reading or recognizing printed or written characters or for recognizing graphics technology field, Especially the small-sample meta-learning training method for medical image classification processing artificial intelligence. Background technique [0002] At present, medical imaging including X-ray imaging, CT imaging, magnetic resonance imaging, ultrasound imaging, and nuclear medicine imaging allows doctors to understand the changes in the internal shape, function, and metabolism of the patient's body in addition to contact and anatomy. have an important role. Medical imaging plays an extremely important role in medical clinical diagnosis, and modern medicine cannot do without medical imaging technology. The ...

AI Technical Summary

Problems solved by technology

[0020] The purpose of the present invention is to provide a small-sample meta-learning training method for medical image classification and artificial intelligence, aiming at the phenomenon of relatively few data in the medical field, to solve the problem of small medical samples, to realize small-sample classification by using meta-learning methods, and to improve the accuracy of difficult diseases. The practicability and reliability of artificial intelligence automatic detection in the field can effectively enhance the accuracy of medical image classification

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