Large-scale Annotation of Biomedical data and Expert Label Synthesis
LABELS 2017
- URL: http://www.labels2017.org
- Event Date: 2017-09-14 ~ 2017-09-14
- Submission Date: 2017-06-12
- Location: Quebec, Canada
CALL FOR PAPERS: 2nd Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis (MICCAI LABELS)
There has been an increasing interest of the MICCAI community for data-driven methods such as supervised learning techniques. The effectiveness of such approaches often depends on their access to sufficiently large quantities of labelled data of good quality. Despite the increasing amount of acquired clinical data, the availability of ready-to-use annotations is very limited.
The goals of LABELS are to
raise awareness on the importance of a methodological acquisition of training data and a careful design of the labelling procedures
promote the development and scientific exchange of algorithms that focus on assisting the annotation process
To this end, we call for submissions addressing the labelling/annotation task by means of approaches from the following fields:
Active learning
Semi-supervised learning
Reinforcement learning
Domain adaptation and transfer learning
Crowdsourcing
Fusion of labels from different sources
Data augmentation
Modelling of label uncertainty
Visualization and human-computer interaction
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 (http://www.springer.com/lncs), 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: http://www.miccai2017.org/submissions).
INVITED SPEAKERS
-Danna Gurari, University of Texas at Austin
-Tanveer Syeda-Mahmood, IBM
-Emanuele Trucco, University of Dundee
IMPORTANT DATES
24 April 2017 Submissions open
12 June 2017 Deadline for paper/abstract submission
7 July 2017 Notification of acceptance
17 July 2017 Camera-ready deadline
14 September 2017 Workshop in Quebec, Canada
For more information, please check our website, http://labels2017.org
ORGANIZERS
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
Marc-André Carbonneau, École de Technologie Supérieure (ETS), Canada
Gustavo Carneiro, University of Adelaide, Australia
SPONSOR
understandAI GmbH - https://understand.ai/
There has been an increasing interest of the MICCAI community for data-driven methods such as supervised learning techniques. The effectiveness of such approaches often depends on their access to sufficiently large quantities of labelled data of good quality. Despite the increasing amount of acquired clinical data, the availability of ready-to-use annotations is very limited.
The goals of LABELS are to
raise awareness on the importance of a methodological acquisition of training data and a careful design of the labelling procedures
promote the development and scientific exchange of algorithms that focus on assisting the annotation process
To this end, we call for submissions addressing the labelling/annotation task by means of approaches from the following fields:
Active learning
Semi-supervised learning
Reinforcement learning
Domain adaptation and transfer learning
Crowdsourcing
Fusion of labels from different sources
Data augmentation
Modelling of label uncertainty
Visualization and human-computer interaction
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 (http://www.springer.com/lncs), 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: http://www.miccai2017.org/submissions).
INVITED SPEAKERS
-Danna Gurari, University of Texas at Austin
-Tanveer Syeda-Mahmood, IBM
-Emanuele Trucco, University of Dundee
IMPORTANT DATES
24 April 2017 Submissions open
12 June 2017 Deadline for paper/abstract submission
7 July 2017 Notification of acceptance
17 July 2017 Camera-ready deadline
14 September 2017 Workshop in Quebec, Canada
For more information, please check our website, http://labels2017.org
ORGANIZERS
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
Marc-André Carbonneau, École de Technologie Supérieure (ETS), Canada
Gustavo Carneiro, University of Adelaide, Australia
SPONSOR
understandAI GmbH - https://understand.ai/
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