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Overview of ImageCLEFcaption 2017 – Image Caption Prediction and Concept Detection for Biomedical Images

Eickhoff, Carsten and Schwall, Immanuel and Garcia Seco De Herrera, Alba and Müller, Henning (2017) Overview of ImageCLEFcaption 2017 – Image Caption Prediction and Concept Detection for Biomedical Images. In: CLEF Conference and Labs of the Evaluation Forum, CLEF 2017, 2017-09-11 - 2017-09-14, Dublin; Ireland.

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Abstract

This paper presents an overview of the ImageCLEF 2017 caption tasks on the analysis of images from the biomedical literature. Two subtasks were proposed to the participants: a concept detectiontask and caption prediction task, both using only images as input. Thetwo subtasks tackle the problem of providing image interpretation by extracting concepts and predicting a caption based on the visual information of an image alone. A dataset of 184,000 figure-caption pairs from the biomedical open access literature (PubMed Central) are provided asa testbed with the majority of them as training data and then 10,000 as validation and 10,000 as test data. Across two tasks, 11 participating groups submitted 71 runs. While the domain remains challenging and the data highly heterogeneous, we can note some surprisingly good results of the difficult task with a quality that could be beneficial for health applications by better exploiting the visual content of biomedical figures.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: CLEF 2017 working Notes - Notes: location: Dublin, Ireland
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 22 Jan 2020 11:43
Last Modified: 22 Jan 2020 12:15
URI: http://repository.essex.ac.uk/id/eprint/22217

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