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GRAIL@MICCAI2023 - 1st CALL FOR PAPERS
5th Workshop on GRaphs in biomedicAl Image anaLysis in conjunction with MICCAI, Vancouver
Abstract/Paper submission deadline: 7th/14th July 2023
See details https://grail-miccai.github.io/
GRAIL 2023 is the fifth international workshop on GRaphs in biomedicAl Image anaLysis, organized as a satellite event of MICCAI 2023 in Vancouver (online event). Applications of GNNs in medicine are numerous, ranging from medical imaging and shape understanding, brain connectomics, population models, and patient multi-omics to the discovery and design of novel drugs and therapeutics. With this workshop, we aim to provide a platform for understanding and application of graph-based models as versatile and powerful tools in biomedical image analysis and beyond. Compared to our previous GRAIL installments, we specifically encourage submissions in the areas of explainable GNNs, graph models in computer-aided surgery/intervention, unstructured medical big data, and semantic knowledge (scene/knowledge graphs).
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/gp/computer-science/lncs/conference-proceedings-guidelines).
All accepted full papers will be published as a joint MICCAI Workshop proceeding in the https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines.
Submissions are welcomed at:
https://cmt3.research.microsoft.com/GRAIL2023/Submission/Index
The submission system will open on 1st June 2023
Important Dates
Abstract Submission: 07 July 2023
Paper Submission deadline: 14 July 2023
Reviews due: 31 July 2023
Author Notification: 04 August 2023
Camera-ready papers due: 11 August 2023
Workshop proceedings due TBA
Workshop date: TBA
Conference Topics
In more detail, the scope of methodology topics includes but is not limited to:
Deep/machine learning on graphs with regular and irregular structures
Probabilistic graphical models for biomedical data analysis
Signal processing on graphs for biomedical image analysis, including non-learning based approaches
Explainable AI (XAI) methods in geometric deep learning
Big data analysis with graphs
Semantic graph research in medicine: Scene graphs and knowledge graphs
Modeling and applications of graph symmetry and equivariance
Graph generative models
Combination of graphs with other SOTA domains (e.g. self-supervised learning, federated learning)
Applications covered include but are not limited to:
Image segmentation, registration, classification
Graph representations in pathology imaging and whole-slide image analysis
Graph-based approaches in intra-operative surgical support
Graph-based shape modeling and dimensionality reduction
Graphs for large scale patient population analyses
Combining multimodal/multi-omics data through graph structures
Graph analysis of brain networks and connectomics
Organizing committee
Seyed-Ahmad Ahmadi, NVIDIA, Germany
Anees Kazi, Technical University Munich (TUM), Germany
Kamilia Mullakaeva, Technical University Munich (TUM), Germany
Bartlomiej Papiez, University of Oxford, UK
Additional links
Webpage: https://grail-miccai.github.io/
Email: [log in to unmask]
Twitter: https://twitter.com/GRAIL2023
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