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LABELS 2018 : Large-scale Annotation of Biomedical data and Expert Label Synthesis


Link: http://www.
When Sep 16, 2018 - Sep 16, 2018
Where Granada, Spain
Submission Deadline Jun 11, 2018
Notification Due Jul 13, 2018
Final Version Due Jul 27, 2018
Categories    machine learning   medical imaging   crowdsourcing   deep learning

Call For Papers

CALL FOR PAPERS: 3nd Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis (MICCAI LABELS)

With the widespread use of data-intensive supervised machine learning methods within the medical image computing community, a growing pressure has mounted to generate vast quantities of quality annotations. Unsurprisingly, in response to the need for very large volumes of training data for deep learning systems, the demand for new methodology to gather vast amounts of annotations in efficient, coherent and safe ways has only surged.

In this context, the proposed third edition of the LABELS workshop ( focuses on bringing together researchers in the field to discuss and principles of training data acquisition and the careful design of labelling procedures. A second goal is to promote the development and scientific exchange of algorithms that focus on assisting the annotation process by making them, for example, more general, more accurate, faster or more intuitive for the medical experts. To this end, we propose a diverse program including keynote talks from world-renowned experts and paper submissions addressing the labelling/annotation task by means of methods from domains such as:

– Active learning
– Semi-supervised learning
– Reinforcement learning
– Domain adaptation and transfer learning
– Crowdsourcing
– Learning from multiple annotation modalities
– Deep learning architectures
– Fusion of labels from different sources
– Data augmentation
– Modelling of label uncertainty
– Visualization and human-computer interaction for annotation generation
– Verification and validation of annotations

Because data annotation and expert labelling is strongly grounded in practical considerations, we welcome not only research contributions, but also encourage submitters to share war stories and practical feedback on successful or insightfully unsuccessful data collection experiences in real world settings. We call for two types of contributions:

– Full papers, 8-10 pages which will be published as proceedings in Lecture Notes in Computer Science (, to be presented as oral presentations and/or posters.
– Abstracts, 1-2 pages covering recent published work, preliminary results and/or open problems, to be presented during short spotlight presentations and/or posters.
The reviewing process will be single blind. The format for paper/abstract submission is the same as the main conference. (Guidelines:


– Leo Joskowicz, (Hebrew University, Israel)
– Elizabeth Krupinski (Emory, USA)


18 April 2018 – Submission website open
11 June 2018 – Deadline for submissions
13 July 2018 – Notification to authors
27 July 2018 – Camera-ready papers
16 September 2018 – Workshop day, Granada Spain

For more information, please check our website,


– Raphael Sznitman, University of Bern, Switzerland
– Veronika Cheplygina, Eindhoven University of Technology (TU/e), The Netherlands
– Diana Mateus, Technische Universität München (TUM), Germany
– Lena Maier-Hein, German Cancer Research Center (DKFZ), Germany
– Eric Granger, École de Technologie Supérieure (ETS), Canada
– Pierre Jannin, INSERN Rennes, France
– Emanuele Trucco, University of Dundee, Scotland


Shadi Albarqouni, Technische Universität München
Marleen de Bruijne, Erasmus MC Rotterdam / University of Copenhagen
Weidong Cai, University of Sydney
Filipe Condessa, Instituto Superior Tecnico
Nishikant Deshmukh, Johns Hopkins University
Michael Götz, German Cancer Research Center
Joseph Jacobs, University College London
Nicholas Heller, University of Minnesota
Ksenia Konyushova, Ecole Polytechnique Federale de Lausanne
Agata Mosinska, Ecole Polytechnique Federale de Lausanne
Silas Ørting, University of Copenhagen
Nicolas Padoy, IHU-Strasbourg
Joao Papa, Sao Paulo State University
Loic Peter, University College London
Roger Tam, University of British Columbia


RetinAi –

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