MILLanD2022: Medical Image Learning with Limited and Noisy Data Medical Image Computing and Computer Assisted Interventions (MICCAI 2022) Singapore, Singapore, September 16-23, 2022 |
Conference website | https://zghada90.wixsite.com/milland |
Submission link | https://easychair.org/conferences/?conf=milland2022 |
Abstract registration deadline | June 20, 2022 |
Submission deadline | June 25, 2022 |
This MICCAI workshop brings together machine learning scientists, biomedical engineers, and medical doctors to discuss challenges and limitations of current deep learning methods applied to medical data and present new methods for training models using imperfect and limited real-world medical data. Topics of special interest include, but are not limited to:
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Data annotation strategies, data augmentation strategies
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Approaches for automated medical image annotation/labeling
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Approaches for medical image augmentation/synthesis
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Approaches for learning noise invariant features
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Learning with noisy/corrupted data or uncertain labels
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Optimal data and source selection for effective training
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Weakly-supervised, semi-supervised, self-supervised learning
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Transfer learning strategies and modality-specific representation
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Learning in real-world and open environment scenarios
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Zero-shot learning and one-shot learning
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New datasets and metrics to evaluate the above methods
Submission Guidelines
We welcome short or full length papers:
Posters: We encourage the submissions of short papers (2-4 pages) describing new, previously, or concurrently published research or work-in-progress. The paper will be published in the workshop webpage and presented as posters.
Full papers: We encourage the submissions full length papers (8 pages excluding references) describing new work that has not been previously published. Accepted papers will be presented as orals and published with MICCAI Proceedings in the Springer LNCS Series.
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Please carefully read MICCAI Submission Guidelines when preparing your submission.
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The reviewing process is double-blind. Authors should avoid providing information that may identify them in the acknowledgments (e.g., grant IDs) or citations. Avoid providing links to websites that may identify any of the authors. Violation of any of these guidelines may lead to rejection without further review.
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The papers will be reviewed by at least three referees. All papers will be evaluated by external reviewers and area chairs.
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Authors of all accepted papers will be invited to present their work either as a poster or an oral.
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We strongly encourage authors to highlight the contribution of all authors in the paper.
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We strongly encourage authors to improve the reproducibility of their research along three directions: open data, open implementations, and appropriate evaluation design and reporting.
Paper template: Please use the most recent MICCAI2022 template.
Committees
Program Committee
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Sema Candemir, Ph.D., Ohio State University
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Alba García Seco Herrera, Ph.D., University of Essex
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Feng Yang, Ph.D. ,National Library of Medicine
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Mustafa Hajij, Ph.D., Santa Clara University
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Peng Guo, Ph.D.,National Library of Medicine
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Mu Zhou, Ph.D., Stanford University
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Ismini Lourentzou, Ph.D., Virginia Tech University
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Alexandros Karargyris, Ph.D., Institute of Image-Guided Surgery (IHU Strasbourg)
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Sirajus Salekin, Ph.D., University of South Florida
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Rahul Paul, Ph.D., Harvard Medical School
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Prasanth Ganesan, Ph.D., Stanford Cardio. Medicine
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Sudhir Sornapudi, Ph.D., Corteva Agriscience
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Lokendra Thakur, Ph.D. , MIT & Harvard Broad Institute
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Anabik Pal, Ph.D., SRM University, AP
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Lihui Wang, Ph.D.,, Guizhou University
Organizing committee
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Ghada Zamzmi, Ph.D., National Library of Medicine
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Sameer Antani, Ph.D., National Library of Medicine
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Marius George Linguraru, Ph.D., George Washington University
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Ulas Bagci, Ph.D., Northwestern University
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Zhiyun Xue, Ph.D., National Library of Medicine
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Sivaramakrishnan Rajaraman, Ph.D., National Library of Medicine
Contact
All questions about submissions should be emailed to: alzamzmiga@nih.gov