The automatic clinical caption generation problem is referred to as proposed model combining the analysis of frontal chest X-Ray scans with structured patient information from the radiology records. We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed combination of these models generates a textual summary with the essential information about pathologies found, their location, and the 2D heatmaps localizing each pathology on the original X-Ray scans. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO. The results measured with the natural language assessment metrics prove their efficient applicability to the chest X-Ray image captioning.
@article{arxiv.2209.13983,
title = {Medical Image Captioning via Generative Pretrained Transformers},
author = {Alexander Selivanov and Oleg Y. Rogov and Daniil Chesakov and Artem Shelmanov and Irina Fedulova and Dmitry V. Dylov},
journal= {arXiv preprint arXiv:2209.13983},
year = {2022}
}
Comments
13 pages, 3 figures, The work was completed in 2021