A Survey on Biomedical Image Captioning

John Pavlopoulos, Vasiliki Kougia, Ion Androutsopoulos


Abstract
Image captioning applied to biomedical images can assist and accelerate the diagnosis process followed by clinicians. This article is the first survey of biomedical image captioning, discussing datasets, evaluation measures, and state of the art methods. Additionally, we suggest two baselines, a weak and a stronger one; the latter outperforms all current state of the art systems on one of the datasets.
Anthology ID:
W19-1803
Volume:
Proceedings of the Second Workshop on Shortcomings in Vision and Language
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Raffaella Bernardi, Raquel Fernandez, Spandana Gella, Kushal Kafle, Christopher Kanan, Stefan Lee, Moin Nabi
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
26–36
Language:
URL:
https://aclanthology.org/W19-1803
DOI:
10.18653/v1/W19-1803
Bibkey:
Cite (ACL):
John Pavlopoulos, Vasiliki Kougia, and Ion Androutsopoulos. 2019. A Survey on Biomedical Image Captioning. In Proceedings of the Second Workshop on Shortcomings in Vision and Language, pages 26–36, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
A Survey on Biomedical Image Captioning (Pavlopoulos et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-1803.pdf
Code
 nlpaueb/bio_image_caption +  additional community code
Data
IU X-RayPeir Gross