Medical information cross-modal hash coding learning method based on generative adversarial network

Abstract

The invention relates to a medical information cross-modal hash coding learning method based on a generative adversarial network, and belongs to the technical field of medical information processing and information retrieval. According to the method, a generative adversarial network is adopted to learn hash codes of chest CT images and texts, and the learned hash codes are constrained through a semantic similarity matrix. Finally, accurate hash codes are learned, and semantic association between the two modes is successfully achieved. According to the method, on the basis of single-layer fine-grained pulmonary nodule features, more complete feature information of the three-dimensional pulmonary nodule is extracted, and a hash code generation model is obtained by adopting a supervised training mode in the method, so that relatively high accuracy is realized in cross-modal retrieval.

Description

technical field [0001] The invention relates to a method for learning cross-modal hash coding of medical information based on a generative confrontation network, and belongs to the technical field of medical information processing and information retrieval. Background technique [0002] Research on computer-aided diagnosis through deep learning to solve some problems in the medical field has attracted more and more attention from researchers and doctors, and lung cancer is currently one of the most widely studied diseases. Radiologists detect early lung cancer by screening nodules on chest CT images, and save the detection results in text form as the basis for clinicians' diagnosis. In the early days, the diagnosis of the malignancy of pulmonary nodules was mainly by setting thresholds, observing the changes in nodule volume at different times, and finally using a standard formula to evaluate the growth rate of nodules. At present, scholars have carried out multi-modal retr...

AI Technical Summary

Problems solved by technology

Both text-based and content-based medical image retrieval are single-modal retrieval, which can only rely on the semantic information or even annotation information of a single modality for retrieval between modal data, and cannot make full use of the hidden semantic information between different modalities. However, the above-mentioned existing cross-modal retrieval methods based on deep learning cannot achieve a good semantic correlation in terms of the correlation between modalities.

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