Medical image picture analysis method and device, electronic equipment and readable storage medium

Abstract

The invention relates to artificial intelligence, and discloses a medical image picture analysis method, which comprises the steps of training a pre-constructed deep learning network model by utilizing a preset part disease historical picture set to obtain a disease recognition model; constructing a distillation loss function according to the disease identification model and a pre-constructed initial diagnosis model; performing distillation training on the initial diagnosis model according to the distillation loss function to obtain a first diagnosis model; performing training and output adjustment on the first diagnosis model according to a preset diagnosis target to obtain a target diagnosis model; and when the to-be-analyzed medical image picture is received, analyzing the to-be-analyzed medical image picture by utilizing the target diagnosis model to obtain an analysis result. The invention further relates to a block chain technology. Data of the training model can be stored in theblock chain. The invention further provides a medical image picture analysis device, electronic equipment and a computer readable storage medium. According to the invention, the consumption of modelcomputing resources for medical image picture analysis can be reduced.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a medical image analysis method, device, electronic equipment and readable storage medium. Background technique [0002] With the development of artificial intelligence, it has become more and more common to use the medical image analysis model based on deep learning model training to analyze medical image pictures to assist in disease diagnosis. However, training deep learning models usually requires a high hardware threshold and cannot Migrate to mobile terminals or places where computing resources are scarce. If you directly train a lightweight model, not only the feature extraction ability is weak, but also the accuracy is low. Therefore, a medical image that maintains high accuracy and does not require too many computing resources is required. Image analysis methods. Contents of the invention [0003] The present invention provides a medical image analysis method, d...

AI Technical Summary

Problems solved by technology

[0002] With the development of artificial intelligence, it has become more and more common to use the medical image analysis model based on deep learning model training to analyze medical image pictures to assist in disease diagnosis. However, training deep learning models usually requires a high hardware threshold and cannot Migrate to mobile terminals or places where computing resources are scarce. If you directly train a lightweight model, not only the feature extraction ability is weak, but also the accuracy is low. Therefore, a medical image that maintains high accuracy and does not require too many computing resources is required. Image Analysis Method

Images