Multi-modal nuclear magnetic resonance image case report automatic generation method

A nuclear magnetic resonance, automatic generation technology, applied in medical reports, medical automatic diagnosis, medical images, etc., can solve problems such as lack of correlation sorting, results are not readable text, etc., to ease the work of radiologists.

Active Publication Date: 2018-08-07
FUDAN UNIV
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Problems solved by technology

In the traditional method, the classification of medical record attributes is obtained through image feature analysis. It is nece

Method used

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  • Multi-modal nuclear magnetic resonance image case report automatic generation method
  • Multi-modal nuclear magnetic resonance image case report automatic generation method
  • Multi-modal nuclear magnetic resonance image case report automatic generation method

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Embodiment approach

[0040] (1) Data preprocessing

[0041] (1.1) Image data: Use N4ITK and Nyul to adjust the brightness of the image, and get the following figure 1 The results shown; the image is divided into several adjacent areas of 44*44*20, and a small block of 132*132*108 is extracted for each area, that is, 44 padding is added in three directions (for the area outside the boundary of the original image The area is filled with 0); the ground truth of the image segmentation result is divided into 44*44*20 areas. (Note: In order to increase the size of the training set, the 44*44*20 area can be overlapped)

[0042] (1.2) Text data: 1) Remove repeated spaces and punctuation marks in the text; 2) Treat the text as a sample with a period as a unit. 3) Use FoolNLTK to segment the text, and use gensim to get the dictionary and word vector model (set the dimension of the vector to 512). For example ['skull base','structure',',','signal','no disease','rationality','change'], 'morphology' can be ...

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Abstract

The invention belongs to the medical data analysis and intelligent processing technical field, and specifically relates to a multi-modal nuclear magnetic resonance image case report automatic generation method employing a deep learning model; the method comprises the following steps: importing an attention matrix on the basis of using a convolution nerve network to extract image characteristics; giving different weights on characteristics of different positions via point multiplication operation, thus obtaining image characteristics under different attentions; using a long-short period memorycycle nerve network to form a subject vector of each sentence in a case report according to the image characteristics under different attentions; using another long-short period memory cycle nerve network to form each word according to the subject vector of the sentence; connecting the words so as to obtain the final case report. The method can automatically form a description text of a magnetic resonance image case without using a case template, thus providing deep meanings for mitigating works of radiology department doctors and building an intelligent computer auxiliary diagnosis system.

Description

technical field [0001] The invention belongs to the technical field of medical data analysis and intelligent processing, and in particular relates to a method for automatically generating medical record reports in natural language for nuclear magnetic resonance images. Background technique [0002] According to Hao Jie, President of the Cancer Hospital of the Chinese Academy of Medical Sciences, Director of the National Cancer Center, and Academician of the Chinese Academy of Sciences, at the annual meeting of radiation oncology on "The Status and Trends of Cancer in China", it can be found that the current cancer mortality rate in my country is It is higher than the global average of 17%. The reasons include the lack of an effective tumor screening mechanism and the lack of an efficient and homogeneous tumor diagnosis and treatment system, which hinders early diagnosis and effective later treatment of patients. Precision medicine and medical image-aided diagnosis systems bas...

Claims

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Application Information

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IPC IPC(8): G16H15/00G16H30/20G16H50/20
CPCG16H15/00G16H30/20G16H50/20
Inventor 熊贇陆周涛朱扬勇
Owner FUDAN UNIV
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