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A method for automatic generation of multimodal MRI medical record reports

A nuclear magnetic resonance, automatic generation technology, applied in medical reports, medical automated diagnosis, medical images, etc., can solve the problems of non-readable text and lack of correlation ranking.

Active Publication Date: 2021-08-20
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 necessary to use a separate model for each attribute, and the results obtained are not readable text and lack of correlation ranking.

Method used

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  • A method for automatic generation of multimodal MRI medical record reports
  • A method for automatic generation of multimodal MRI medical record reports
  • A method for automatic generation of multimodal MRI medical record reports

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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 technical field of medical data analysis and intelligent processing, and in particular relates to a method for automatically generating multi-mode nuclear magnetic resonance image medical record reports. The present invention adopts a deep learning model, wherein an attention matrix is ​​introduced on the basis of using a convolutional neural network to extract image features, and different weights are given to features at different positions through dot multiplication operations to obtain image features under different attentions; then Use a long short-term memory recurrent neural network to generate the topic vector of each sentence in the medical report according to the image features under different attention; then use another long short-term memory recurrent neural network to generate each word according to the topic vector of the sentence; then Connect these words to get the final medical report. The invention automatically generates the description text in the medical image medical record without the medical record template, which has far-reaching significance for alleviating the work of radiologists and building an intelligent computer-aided 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...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G16H15/00G16H30/20G16H50/20
CPCG16H15/00G16H30/20G16H50/20
Inventor 熊贇陆周涛朱扬勇
Owner FUDAN UNIV