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GRAIL 2020 : 3rd Workshop on GRaphs in biomedicAl Image anaLysis @ MICCAI2020

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Link: https://grail-miccai.github.io/
 
When Oct 8, 2020 - Oct 8, 2020
Where Lima, Peru
Submission Deadline Jul 7, 2020
Notification Due Jul 21, 2020
Final Version Due Jul 28, 2020
Categories    medical image analysis   graphs   machine learning   generative graphical models
 

Call For Papers

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GRAIL@MICCAI2020 - 1st CALL FOR PAPERS
3rd Workshop on GRaphs in biomedicAl Image anaLysis
In conjunction with: MICCAI, Lima, Peru
Deadline: 30th June 2020 https://grail-miccai.github.io/

GRAIL 2020 is the 3rd Workshop on GRaphs in biomedicAl Image anaLysis organised as a satellite event at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2020) In Lima, Peru. The event is hosted to communicate research progress within the community interested in graph-based methods and their potential applications for biomedical image analysis. Its goal is to bring together scientists that use and develop graph-based models for the analysis of biomedical images and encourage the exploration of graph-based models for challenging clinical problems within a variety of biomedical imaging contexts.
The workshop will feature invited keynote speakers, as well as oral and poster presentations of original research.

Submission guidelines
Authors are invited to submit papers describing original research with length between 8 to 12 pages (including text, figures and tables, and references). Papers should be anonymous and formatted using the LNCS template (https://www.springer.com/gb/computer-science/lncs).
All accepted full papers will be published as a joint MICCAI Workshop proceedings in the Springer Lecture Notes in Computer Science (LNCS).

Submissions are welcomed at: https://cmt3.research.microsoft.com/GRAIL2020/

Important Dates
Paper Submission deadline: 7th July 2020
Author Notification: 21st July 2020
Camera-ready papers due: 28th July 2020
Workshop date: 8th October 2020

Conference Topics
The covered topics include but are not limited to:
Deep/machine learning on graphs with regular and irregular structures
Probabilistic graphical models for biomedical image analysis
Discrete and continuous optimization for graphical models
Signal processing on graphs for biomedical image analysis
Deep/machine learning on structured and unstructured graphs
Convolutional neural networks on graphs
Graphs for large scale population analysis
Graph-based shape modeling and dimensionality reduction
Combining imaging and non-imaging data through graph structures
Graph-based generative models for biomedical image analysis
Graph spectral methods
Algorithms on graphs
Graphs in neuroimaging
Applications of graph-based models and algorithms to biomedical image analysis tasks (segmentation, registration, classification, etc.)
Generative graphical models for data synthesis and augmentation

Keynote speakers
Dr. Ahmad Ahmadi, TUM, Munich, Germany
Prof. Hervé Lombaert, ETS Montreal / Canada

Organising committee
Hamid Fehri, University of Oxford
Bartek Papiez, University of Oxford,
Enzo Ferrante, CONICET / Universidad Nacional del Litoral,
Sarah Parisot, AimBrain,
Aristeidis Sotiras, Washington University in St Louis.

Additional links
Webpage: https://grail-miccai.github.io/
Email: grail.miccai@gmail.com
Twitter: https://twitter.com/grail2020

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