Medical image expression generating system, training method and expression generating method thereof

Abstract

The invention discloses a medical image expression generating system, a training method and an expression generating method thereof. After a medical image acquisition unit acquires a two-dimensional medical image, a convolutional neural network unit extracts the image characteristic of the medical image and converts the image characteristic to an image characteristic vector, and then outputs the image characteristic vector to a pre-established first vector space. A circulating neural network unit determines a semantic characteristic vector which corresponds with the image characteristic vector according to a correspondence between the image characteristic vector included in the pre-established first vector space and a semantic characteristic vector included in a second vector space, and furthermore performs outputting. An expression output unit converts the semantic characteristic vector which matches the image characteristic vector to a corresponding natural language and performs outputting. Therefore the expression generating system realizes simple reading and analyzing to the medical image and furthermore has advantages of improving image reading efficiency, improving image reading quality and greatly reducing misdiagnosing probability.

Description

technical field [0001] The invention relates to the field of computers, in particular to a medical image representation generation system, its training method and representation generation method. Background technique [0002] In order to improve the accuracy of disease diagnosis, various techniques for displaying human body images have emerged, such as nuclear magnetic resonance, electronic computer tomography (Computed tomography, CT), X-ray scanning and B-scan Ultrasonography (B-scanUltrasonography) scanning, etc., making Through these technologies, some key information can be accurately obtained, thereby improving the accuracy of disease diagnosis. However, with the substantial increase of annual medical imaging data, the workload of film readers has also become extremely heavy, and since the annual growth of medical image data far exceeds the growth of radiologists, each film reader Physicians have to read nearly a thousand medical images every day, and the overload of...

AI Technical Summary

Problems solved by technology

However, with the substantial increase of annual medical imaging data, the workload of film readers has also become extremely heavy, and since the annual growth of medical image data far exceeds the growth of radiologists, each film reader Physicians have to read nearly a thousand medical images every day, and the overload of work greatly increases the chance of misdiagnosis of diseases

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