Brain CT medical report generation method based on hierarchical recurrent neural network decoding

Abstract

The invention discloses a brain CT medical report generation method based on hierarchical recurrent neural network decoding. The method comprises the steps that firstly, a brain CT image and corresponding medical report data are acquired and preprocessed; constructing a feature extractor, completing coding of the brain CT image data, obtaining coding features and fault block visual features, constructing an orientation keyword predictor, and extracting orientation keyword semantic features Fs of the brain CT image data I; constructing a hierarchical recurrent neural network language model, performing hierarchical decoding on the model by using Fs, and generating a medical report sentence by sentence; training and optimizing the model; preprocessing the brain CT to be predicted; extracting coding features and fault block visual features of the brain CT to be predicted by using the orientation keywords; extracting semantic features by using the orientation keywords; the language model generates the predicted medical report sentence by sentence using the coding features, the fault block visual features, and the semantic features.

Description

technical field [0001] The present invention uses relevant technical means in the two fields of computer vision and natural language processing, and aims at the automatic report generation task in the field of medical image analysis, and designs a medical report generation method for hierarchical cyclic neural network decoding. Background technique [0002] The task goal of automatic generation of medical reports is to input a set of brain CT images containing spatial sequence relationships, and the computer can automatically generate several sentences describing the contents of the images as corresponding medical reports, which requires the computer to have the ability to understand brain CT images , also needs to have the language organization ability to express the image content, which is a research hotspot in the current medical image analysis. [0003] With the rise of various technologies in the field of artificial intelligence, computer-assisted medical image analysis...

AI Technical Summary

Problems solved by technology

Among them, machine learning techniques have played an important role in tasks such as classification and segmentation. However, traditional machine learning techniques have shown limited capabilities when dealing with large-scale annotated or unlabeled data.

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