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ImageCLEF 2017


ImageCLEF 2017 is an evaluation campaign which is being organized as part of the CLEF initiative labs. The campaign offers several research tasks that welcome participation from teams around the world. The results of the campaign appear in the working notes proceedings, published by CEUR Workshop Proceedings ( Selected contributions among the participants, will be invited for publication in the following year in the Springer Lecture Notes in Computer Science (LNCS) together with the annual lab overviews.

For the 2017 edition, ImageCLEF organises 3 main tasks with a global objective of benchmarking lifelogging retrieval and summarization, tuberculosis type prediction from CT images and bio-medical image caption prediction; and a pilot task on remote sensing image analysis.

Target communities of the tasks involve (but are not limited to): information retrieval (text, vision, audio, multimedia, social media, sensor data), machine learning (including deep learning), data mining, natural language processing, image/video processing, remote sensing, with special attention to the challenges of multi-modality, multi-linguality, and interactive search.

Download the ImageCLEF 2017 call for participation flyer here.

Stay tuned with us for the latest information and updates by joining us on the ImageCLEF social media accounts: Twitter @imageclef, and Facebook (click on the link).

ImageCLEF schedule

Each of the tasks sets its own schedule, so please check the corresponding task webpage for specific dates. A (tentative) global schedule can be found below:
  • 14.11.2016: registration opens for all ImageCLEF tasks (until 21.04.2017)
  • 14.11.2016: development data release starts (depends on the task)
  • 20.03.2017: test data release starts (depends on the task)
  • 01.05.2017: deadline for submitting the participants runs (depends on the task)
  • 15.05.2017: release of the processed results by the task organizers (depends on the task)
  • 31.05.2017 26.05.2017: deadline for submission of working notes papers by the participants
  • 19.06.2017: notification of acceptance of the working notes papers
  • 01.07.2017: camera ready working notes papers
  • 11-14.09.2017: CLEF 2017, Dublin, Ireland

The CLEF Conference

CLEF 2017 CLEF Initiative
ImageCLEF lab and all its tasks are part of the Conference and Labs of the Evaluation Forum: CLEF 2017. CLEF 2017 consists of an independent peer-reviewed workshops on a broad range of challenges in the fields of multilingual and multimodal information access evaluation, and a set of benchmarking activities carried in various labs designed to test different aspects of mono and cross-language Information retrieval systems. More details about the conference can be found here. Also there is more information about the Clef Initiative.

Participant registration

Registration for ImageCLEF 2017 is now open and will stay open until 21.04.2017. To register please follow the steps below:
Once registered and the signature validated, data access details can be found in the ImageCLEF system -> Collections. Please note that depending on the task, before downloading the data, you may be required for signing some additional data usage agreements. Should you have any questions about the registration process, please contact Mihai Dogariu <dogariu_mihai8(at)>.

The Tasks

ImageCLEF 2017 proposes 3 main tasks and a pilot task:
  • ImageCLEFlifelog: The availability of a large variety of personal devices, such as smartphones, video cameras as well as wearable devices that allow capturing pictures, videos, and audio clips in every moment of our life is creating the need for systems that can automatically analyse the huge amounts of data stored every day in order to categorize, summarize and also query them to retrieve the information that the user may need. The task addresses the problems of lifelogging data retrieval and summarization.
  • ImageCLEFcaption: Interpreting and summarizing the insights gained from medical images such as radiology output is a time-consuming task that involves highly trained experts and often represents a bottleneck in clinical diagnosis pipelines. Consequently, there is a considerable need for automatic methods that can approximate this mapping from visual information to condensed textual descriptions. The task addresses the problem of bio-medical image caption prediction from large amounts of training data.
  • ImageCLEFtuberculosis: The objective of the task is to determine the TB subtypes and drug resistances as much as possible automatically from the volumetric image information (mainly texture analysis) and based on clinical information that is available such as age, gender, etc. Being able to extract the tuberculosis type and drug resistances based on the image data alone can allow to limit lung washing and laboratory analyses to determine the TB type and drug resistances. This can lead to quicker decisions on the best treatment strategy, reduced use of antibiotics and lower impact on the patient.
  • ImageCLEFremote (pilot task - FabSpace 2.0 Exploring Sentinel Copernicus Images): The objective of the task is to explore Earth observation data images (Sentinel Copernicus satellite images) in order to discover unknown information. Before engaging any rescue operation or humanitarian action, NGOs need to evaluate the local population as accurately as possible. Current tools can only do a partial job since additional data needs to be considered for predicting the population correctly. In this task, participants will be given various zones plus some contextual information. They will have to provide the prediction of the population, first as a number, then as a range (min, max).

The Organising Committee

Overall coordination

  • Bogdan Ionescu <bionescu(at)>, University Politehnica of Bucharest, Romania
  • Mauricio Villegas <mauvilsa(at)>, Universitat Politècnica de València, Spain
  • Henning Müller <henning.mueller(at)>, University of Applied Sciences Western Switzerland, Sierre, Switzerland

Technical support

  • Ivan Eggel <ivan.eggel(at)>, University of Applied Sciences Western Switzerland, Sierre, Switzerland
  • Mihai Dogariu <dogariu_mihai8(at)>, University Politehnica of Bucharest, Romania


  • Duc-Tien Dang-Nguyen <duc-tien.dang-nguyen(at)>, Dublin City University, Ireland
  • Luca Piras <luca.piras(at)>, University of Cagliari, Cagliari, Italy
  • Michael Riegler <michaari(at)>, University of Oslo, Norway
  • Cathal Gurrin <cgurrin(at)>, Dublin City University, Ireland
  • Giulia Boato <giulia.boato(at)>, University of Trento, Italy



  • Vassili Kovalev <vassili.kovalev(at)>, Institute for Informatics Minsk, Belarus
  • Henning Müller <henning.mueller(at)>, University of Applied Sciences Western Switzerland, Sierre, Switzerland
  • Yashin Dicente Cid <yashin.dicente(at)>, University of Applied Sciences Western Switzerland, Sierre, Switzerland
  • Alexander Kalinovsky <gakarak(at)>, Institute for Informatics Minsk, Belarus


  • Helbert Arenas <helbert.arenas(at)>, Institut de Recherche en Informatique de Toulouse, UMR5505 CNRS, Université de Toulouse, France
  • Bayzidul Islam <bislam(at)>, Technische Universität Darmstadt, Germany
  • Josiane Mothe <josiane.mothe(at)>, Institut de Recherche en Informatique de Toulouse, UMR5505 CNRS, ESPE, Université de Toulouse, France
  • Dimitrios Soudris <dimitrios.soudris(at)>, Microprocessors and Digital Systems Laboratory of Institute of Communication and Computer Systems, Athens, Greece