Pushing the limits of radiology with joint modeling of visual and textual information

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

3 Citations (Scopus)
8 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationProceedings of the Student Research Workshop
Place of PublicationStroudsburg
PublisherAssociation for Computational Linguistics
Pages28-36
Number of pages9
ISBN (Electronic)9781948087360
DOIs
Publication statusPublished - 15 Jul 2018
EventACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics - Melbourne, Australia
Duration: 15 Jul 201820 Jul 2018

Conference

ConferenceACL 2018 The 56th Annual Meeting of the Association for Computational Linguistics
CountryAustralia
CityMelbourne
Period15/07/1820/07/18

Bibliographical note

Copyright the Publisher 2018. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • computer vision
  • Natural Language Processing
  • Medical Imaging
  • Machine Learning
  • Deep learning
  • health informatics

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