Year: 2023
Type: conference-paper
Abstract: Chest X-ray images are crucial for diagnostics and treatment of various diseases, but analysing them requires highly skilled and experienced medical professionals. This leads to a high burden on radiologists. This paper investigates a method for automatic generation of radiology reports and incorporates visual features extracted from multiple layers of a convolutional visual encoder network. The use of multi-level features can provide more information to the text generator, potentially improving generated reports. Experimental results are reported on the MIMIC-CXR dataset using natural language generation metrics.
Authors: Gordon Waiter, Tryphon Lambrou, Georgios Leontidis, Nir Oren, Teresa Morris, Sharon Gordon, Jade McGowan, Cara Nicolson
Institutions: Helwan University, Nile University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4387099503