Medical Report Generation through Radiology Images: An Overview

Abstract

The interpretation of medical images is a fundamental process for the diagnosis and treatment of patients. This process contributes determining the causes of symptoms as well as monitoring the effects of any treatment. Although the generation of medical reports from images is a complex task, deep learning strategies have been integrated with models that allow this arduous task to be tackled, achieving promising results. This work aims to present a compilation of the most outstanding deep learning strategies focused on the automatic generation of medical radiology reports from X-Ray images. Papers based on DenseNet, ResNet and VGG architectures, in combination with Long Short-Term Memories (LSTMs) and attention models, are analyzed in terms of the pre-processing strategies, databases used, model adaptations, and metric results. All these important findings are summarized in this survey, highlighting those models that reported the highest performance.