Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering

Deepak Gupta, Dina Demner-Fushman


Abstract
In this paper, we present an overview of the MedVidQA 2022 shared task, collocated with the 21st BioNLP workshop at ACL 2022. The shared task addressed two of the challenges faced by medical video question answering: (I) a video classification task that explores new approaches to medical video understanding (labeling), and (ii) a visual answer localization task. Visual answer localization refers to the identification of the relevant temporal segments (start and end timestamps) in the video where the answer to the medical question is being shown or illustrated. A total of thirteen teams participated in the shared task challenges, with eleven system descriptions submitted to the workshop. The descriptions present monomodal and multi-modal approaches developed for medical video classification and visual answer localization. This paper describes the tasks, the datasets, evaluation metrics, and baseline systems for both tasks. Finally, the paper summarizes the techniques and results of the evaluation of the various approaches explored by the participating teams.
Anthology ID:
2022.bionlp-1.25
Volume:
Proceedings of the 21st Workshop on Biomedical Language Processing
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
264–274
Language:
URL:
https://aclanthology.org/2022.bionlp-1.25
DOI:
10.18653/v1/2022.bionlp-1.25
Bibkey:
Cite (ACL):
Deepak Gupta and Dina Demner-Fushman. 2022. Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 264–274, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering (Gupta & Demner-Fushman, BioNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bionlp-1.25.pdf
Data
HowTo100MMedVidQA