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Deep learning in generating radiology reports: A survey.

Maram Mahmoud A Monshi¹, Josiah Poon², Vera Chung²

¹School of Computer Science, University of Sydney, Sydney, Australia; Department of Information Technology, Taif University, Taif, Saudi Arabia.

Artificial Intelligence in Medicine

|May 20, 2020

Summary

This summary is machine-generated.

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Deep learning models are advancing automated radiology reporting by integrating image analysis and natural language processing. This research surveys challenges and future directions for deep learning in radiology.

Area of Science:

Artificial Intelligence
Medical Imaging
Natural Language Processing

Background:

Deep learning (DL) models are increasingly used for automated radiology reporting.
Large medical text/image datasets have enabled significant progress.
Generating coherent radiology reports beyond simple annotations is a key research area.

Purpose of the Study:

To survey the current state of deep learning in automated radiology reporting.
To identify critical challenges in applying DL to radiology text and image data.
To provide future research recommendations in this growing field.

Main Methods:

Utilizing publicly available datasets.
Developing DL models integrating Convolutional Neural Networks (CNNs) for image analysis.

Keywords:

Convolutional neural network Deep learning Natural language processing Radiology Recurrent neural network

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Employing Recurrent Neural Networks (RNNs) for natural language processing (NLP) and generation (NLG).

Main Results:

Current approaches commonly combine CNNs and RNNs for integrated image and text analysis.
Research focuses on understanding data structures, applying DL algorithms, and generating radiology text.
Improving DL models and evaluation metrics are key areas of investigation.

Conclusions:

Automated radiology reporting using DL is a rapidly advancing field.
Bridging visual medical features with radiologist text is a primary goal.
This survey offers insights for researchers in DL and its application to radiology reporting.