Overview of imageCLEF 2018 medical domain visual question answering task

Hasan, Sadid A. (Artificial Intelligence Lab, Philips Research North America, Cambridge, MA, USA) ; Ling, Yuan (Artificial Intelligence Lab, Philips Research North America, Cambridge, MA, USA) ; Farri, Oladimeji (Artificial Intelligence Lab, Philips Research North America, Cambridge, MA, USA) ; Liu, Joey (Artificial Intelligence Lab, Philips Research North America, Cambridge, MA, USA) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Lungren, Matthew (Stanford University, Stanford, CA, USA)

This paper presents an overview of the inaugural edition of the ImageCLEF 2018 Medical Domain Visual Question Answering (VQA-Med) task. Inspired by the recent success of visual question an-swering in the general domain, a pilot task was proposed this year to focus on visual question answering in the medical domain. Given medi-cal images accompanied with clinically relevant questions, participating systems were tasked with answering the questions based on the visual image content. A dataset of 6,413 question-answer pairs accompanied with 2,866 medical images extracted from PubMed Central articles was provided


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Publisher:
Avignon, France, 10-14 September 2018
Date:
2018-09
Avignon, France
10-14 September 2018
Pagination:
8 p.
Published in:
Proceedings of CLEF 2018 Working Notes
ISSN:
1613-0073
External resources:
Appears in Collection:



 Record created 2018-10-31, last modified 2019-06-11

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)