Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information

Sonit Singh


Abstract
Recently, there has been increasing interest in the intersection of computer vision and natural language processing. Researchers have studied several interesting tasks, including generating text descriptions from images and videos and language embedding of images. More recent work has further extended the scope of this area to combine videos and language, learning to solve non-visual tasks using visual cues, visual question answering, and visual dialog. Despite a large body of research on the intersection of vision-language technology, its adaption to the medical domain is not fully explored. To address this research gap, we aim to develop machine learning models that can reason jointly on medical images and clinical text for advanced search, retrieval, annotation and description of medical images.
Anthology ID:
P18-3005
Volume:
Proceedings of ACL 2018, Student Research Workshop
Month:
July
Year:
2018
Address:
Melbourne, Australia
Editors:
Vered Shwartz, Jeniya Tabassum, Rob Voigt, Wanxiang Che, Marie-Catherine de Marneffe, Malvina Nissim
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
28–36
Language:
URL:
https://aclanthology.org/P18-3005
DOI:
10.18653/v1/P18-3005
Bibkey:
Cite (ACL):
Sonit Singh. 2018. Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information. In Proceedings of ACL 2018, Student Research Workshop, pages 28–36, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information (Singh, ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-3005.pdf
Data
Visual Question Answering