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Sci Rep ; 13(1): 4171, 2023 03 13.
Artículo en Inglés | MEDLINE | ID: covidwho-2280462

RESUMEN

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.


Asunto(s)
Benchmarking , Radiología , Humanos , Suministros de Energía Eléctrica , Lenguaje , Tórax
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