Semantic enhanced hash medical image retrieval method based on mixed attention

A medical image and semantic technology, applied in the field of medical image retrieval, can solve problems such as ignoring medical image and label category-level semantics, affecting retrieval performance, and insufficient utilization of advanced semantic information, so as to reduce quantization errors and improve accuracy.

Pending Publication Date: 2022-01-04
WUHAN UNIV OF TECH
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Problems solved by technology

However, most of the existing content-based medical image retrieval methods only learn the relative relationship of medical images to extract deep features, while ignoring the categor

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  • Semantic enhanced hash medical image retrieval method based on mixed attention
  • Semantic enhanced hash medical image retrieval method based on mixed attention
  • Semantic enhanced hash medical image retrieval method based on mixed attention

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[0047] The present invention provides a semantic enhancement hash medical image retrieval method based on mixing attention, first dividing data sets into training sets and test retrieval sets, randomly selects images from training concentration, and then builds the overall network model. The medical triple sample sample is the input of the network model, and finally training the overall network model, and uses the well-trained network to get the search results.

[0048] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and examples.

[0049] like figure 1 As shown, the flow of the embodiments of the present invention includes the following steps:

[0050] Step 1, divide the data set as a training set and test search set.

[0051] The three datasets are used, respectively, the chest X-ray image data set Covid-19Radiography, COVID-19 chest X-ray image combination select data set CurateDX-RAY and the skin mirr...

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Abstract

The invention relates to a semantic enhanced hash medical image retrieval method based on mixed attention. Firstly, a data set is divided into a training set and a test retrieval set, images are randomly selected from the training set to form a medical triple, then an overall network model is constructed, a medical triple sample serves as input of the network model, finally, the overall network model is trained, and a retrieval result is obtained through a trained network. A channel attention module and a space attention module are utilized to form a mixed attention mechanism, and ROI information can be efficiently extracted; the learning process of the Hash codes is restrained by utilizing category-level semantic information, so that different categories of similar Hash codes can be distinguished; and when the depth embedding is mapped to the discrete hash code, the quantization error between the depth embedding and the hash code is reduced by using the quantization loss item, so that the precision of medical image retrieval can be further improved.

Description

technical field [0001] The invention belongs to the field of medical image retrieval, in particular to a semantically enhanced hash medical image retrieval method based on mixed attention. Background technique [0002] With the rapid development of radiography technology, medical data is gradually digitized, and the number of medical images has increased dramatically. In order to better assist medical diagnosis and evaluation, it is critical to mine useful information in large-scale medical images. Therefore, medical image retrieval has attracted extensive attention. [0003] Medical image retrieval can be divided into two categories: text-based medical image retrieval and content-based medical image retrieval. Text-based medical image retrieval appeared in the early days of medical image retrieval. It avoids the analysis of medical image visualization elements, indexes medical images from the aspects of name, size, type, etc., and often queries medical images based on key...

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Application Information

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IPC IPC(8): G16H30/20G06F16/53G06N3/04G06N3/08
CPCG16H30/20G06F16/53G06N3/08G06N3/045
Inventor 陈亚雄李小玉汤一博王凡熊盛武
Owner WUHAN UNIV OF TECH
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