# Medical Image Super-Resolution Using a Generative Adversarial Network

## TL;DR

This paper develops a stroke-focused electronic medical record retrieval system combining text, structured data, and image similarity, achieving over 85% accuracy in top 5 retrievals, aiding diagnosis and training.

## Contribution

It introduces a novel EMR retrieval network integrating multiple data types with a weighted similarity measurement strategy.

## Key findings

- Top 5 retrieval accuracy exceeds 85%
- Effective integration of text, structured data, and images
- Potential for aiding diagnosis and medical training

## Abstract

During the growing popularity of electronic medical records, electronic medical record (EMR) data has exploded increasingly. It is very meaningful to retrieve high quality EMR in mass data. In this paper, an EMR value network with retrieval function is constructed by taking stroke disease as the research object. It mainly includes: 1) It establishes the electronic medical record database and corresponding stroke knowledge graph. 2) The strategy of similarity measurement is included three parts(patients' chief complaint, pathology results and medical images). Patients' chief complaints are text data, mainly describing patients' symptoms and expressed in words or phrases, and patients' chief complaints are input in independent tick of various symptoms. The data of the pathology results is a structured and digitized expression, so the input method is the same as the patient's chief complaint; Image similarity adopts content-based image retrieval(CBIR) technology. 3) The analytic hierarchy process (AHP) is used to establish the weights of the three types of data and then synthesize them into an indicator. The accuracy rate of similarity in top 5 was more than 85\% based on EMR database with more 200 stroke records using leave-one-out method. It will be the good tool for assistant diagnosis and doctor training, as good quality records are colleted into the databases, like Doctor Watson, in the future.