Overview of ImageCLEFcaption 2017 : image caption prediction and concept detection for biomedical images

Eickhoff, Carsten (ETH Zurich, Switzerland) ; Schwall, Immanuel (ETH Zurich, Switzerland) ; García Seco de Herrera, Alba (Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, USA) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

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 detection task and caption prediction task, both using only images as input. The two 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 as a testbed with the majority of them as trainign 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.

Type de conférence:
full paper
Economie et Services
HEG VS HES-SO Valais-Wallis - Haute Ecole de Gestion & Tourisme
Institut Informatique de gestion
Adresse bibliogr.:
Dublin, Ireland, 11-14 September 2017
Dublin, Ireland
11-14 September 2017
10 p.
Publié dans
Proceedings of the CLEF 2017 working notes
Ressource(s) externe(s):
Le document apparaît dans:

 Notice créée le 2017-12-20, modifiée le 2018-08-31

Télécharger le document

Évaluer ce document:

Rate this document:
(Pas encore évalué)