TY - GEN
T1 - Radiology report generation using multi–layer visual representation
AU - Wang, Chenyu
AU - Janjic, Vladimir
AU - McKenna, Stephen
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - https://internal-www.frontiersin.org/books/27th_Conference_on_Medical_Image_Understanding_and_Analysis_2023/10661/all_books
U2 - 10.3389/978-2-8325-1231-9
DO - 10.3389/978-2-8325-1231-9
M3 - Conference contribution
SN - 9782832512319
SP - 251
EP - 256
BT - 27th Conference on Medical Image Understanding and Analysis 2023
PB - Frontiers Media S.A.
ER -