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arXiv · 2209.13983

Medical Image Captioning via Generative Pretrained Transformers

Alexander Selivanov·Oleg Y. Rogov·Daniil Chesakov·Artem Shelmanov·Irina Fedulova·Dmitry V. Dylov
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Abstract

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.

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Alexander Selivanov, Oleg Y. Rogov, Daniil Chesakov, Artem Shelmanov, Irina Fedulova, Dmitry V. Dylov. 2022-09-28. Medical Image Captioning via Generative Pretrained Transformers. https://arxiv.org/abs/2209.13983

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