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LABELS 2020 : The 5th MICCAI Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis


When Oct 4, 2020 - Oct 8, 2020
Where --Lima,-Peru-- Virtual
Submission Deadline Jul 6, 2020
Notification Due Jul 21, 2020
Final Version Due Jul 28, 2020
Categories    computer vision   medical imaging   machine learning   data annotation

Call For Papers

CALL FOR PAPERS: The 5th MICCAI Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis (LABELS 2020)

With the proliferation of data-intensive methods in the MICCAI community, large high-quality databases have become central to the development and effective evaluation of new techniques in this domain. It is therefore essential to the growth of this field that practices around the curation of these databases be communicated and discussed. To this end, a fifth edition of the LABELS workshop will be held in conjunction with MICCAI 2020 in Lima, Peru. (

The goal of LABELS is to bring researchers together to share ideas and resources around the principled collection and annotation of biomedical data. The workshop will feature a diverse program including keynote talks from world-renowned experts in this area, interactive sessions in which participants work together to form coherent positions on relevant issues, and paper submissions addressing any of the following topics

- Crowdsourcing
- Open Data and Open Scientific Practices
- Verification and Validation of Labels
- Data Augmentation
- Semi-supervised learning
- Active learning
- Domain Adaptation, Self-supervision, and Transfer Learning
- Human Factors in Data Curation
- Modeling Label Uncertainty
- Training and Evaluation in the Presence of Noise
- Data Descriptors

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:


Data Descriptors

For the 5th edition of LABELS, we would like to explicitly welcome the submission of papers that describe public datasets -- termed "Data Descriptors". The goal of data descriptors is to provide a peer-reviewed mechanism for researchers to report their data collection and annotation methods in detail, augmenting the already considerable impact that public data has on our field. Good candidates for data descriptors include

- Datasets used for a "Grand Challenge"
- Datasets used for research already reported or under review at a different venue (e.g. MICCAI main)
- New datasets that the authors want to announce to the community

Please note that data descriptors must describe public data.

Code and Data Availability Statements

Also new this year is the requirement that papers include a brief statement describing the availability of the code and data used for the publication. Note that public code and data is not a requirement. Our publishing partner, Springer Nature, provides guidance on data availability statements at the following URL:




30 April 2020 – Submission website open
6 July 2020 – Deadline for papers
20 July 2020 - Deadline for abstracts
3 August 2020 – Notification to authors
17 August 2020 – Camera-ready papers
4 or 8 October 2020 – Workshop day in Lima, Peru


As of now, MICCAI 2020 is scheduled to proceed as an in-person meeting. In the event that MICCAI is canceled or moved online, LABELS will proceed as a virtual event (details TBA) and the proceedings will be published as planned.


- Raphael Sznitman, University of Bern, Switzerland
- Veronika Cheplygina, Eindhoven University of Technology (TU/e), The Netherlands
- Diana Mateus, Technische Universität München (TUM), Germany
- Emanuele Trucco, University of Dundee, Scotland
- Nicholas Heller, University of Minnesota, USA
- Samaneh Abbasi, Eindhoven University of Technology (TU/e), The Netherlands



- Florian Dubost, Erasmus MC, Rotterdam, Netherlands
- Amelia Jimenez-Sanchez, Pompeu Fabra University, Spain
- Obioma Pelka, University of Duisburg-Essen, Germany
- Christoph Friedrich, University of Applied Sciences and Arts Dortmund, Germany
- John Onofrey, Yale University, USA
- Ke Yan, NIH, USA
- Vinkle Srivastav, University of Strasbourg, France
- Loic Peter, University College London, UK
- Jaime Cardoso, Universidade do Porto, Portugal
- Filipe Condessa, Carnegie Mellon University, USA
- Jack Rickman, University of Minnesota, USA
- Bjoern Menze, TUM, Germany
- Roger Tam, University of British Columbia, Canada
- Weidong Cai, University of Sydney, Australia
- Silas Ørting, University of Copenhagen, Denmark
- Joseph Jacobs, University College London, UK
- Joshua Dean, University of Minnesota, USA


- RetinAI
- Auris

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