Medical image captioning via generative pretrained transformers.
Sci Rep
; 13(1): 4171, 2023 03 13.
Article
in English
| MEDLINE | ID: covidwho-2280462
ABSTRACT
The proposed model for automatic clinical image caption generation combines the analysis of radiological scans with structured patient information from the textual records. It uses two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The generated textual summary contains essential information about pathologies found, their location, along with the 2D heatmaps that localize each pathology on the scans. The model has been tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO, and the results measured with natural language assessment metrics demonstrated its efficient applicability to chest X-ray image captioning.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Radiology
/
Benchmarking
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Sci Rep
Year:
2023
Document Type:
Article
Affiliation country:
S41598-023-31223-5
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