MICCAI Workshop: Call for Papers

[Ghada Alzamzmi]

We are happy to announce our upcoming workshop September 18th, 202 in conjunction with MICCAI 202:

Medical Image Learning with Limited & Noisy Data (MILLanD)

 

Join us for this exciting international workshop, which will be held in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), featuring two keynote speakers, Dr. Carola-Bibiane Schönlieb (University of Cambridge) and Dr. Sharon Xiaolei Huang (Penn State University) followed by invited talks, oral and poster presentations, and concluded by panel discussion. In this workshop, we welcome short (2-4 pages) and full papers (8 pages). Short papers can be published works and will be presented as posters; full papers should be original and will be presented as orals and published with MICCAI Proceedings in the Springer LNCS Series. Topics of special interest include, but are not limited to data annotation strategies, data augmentation strategies, approaches for automated medical image annotation/labeling, approaches for medical image augmentation/synthesis, approaches for learning noise invariant features, weakly-supervised, semi-supervised, self-supervised learning, learning in real-world and open environment scenarios, etc.  

 

 Click for More Information about Submission  

Speaker Bios:

 

Dr. Carola-Bibiane Schönlieb

Dr. Schönlieb works on the mathematics behind image analysis. It finds application in all sorts of areas, from medical imaging, such as MRI scans and CT. She is a Professor of Applied Mathematics in the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge. She is also the head of the Cambridge Image Analysis group, Director of the Cantab Capital Institute for Mathematics of Information, Co-Director of the EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging. Her current research interests focus on variational methods and partial differential equations for image analysis, image retrieval and enhancement from corrupted and under sampled measurements, compressed sensing, image restoration, biomedical imaging (MRI, PET/SPECT, microscopy imaging) just to name a few.

 

Dr. Sharon Xiaolei Huang

Dr. Huang is an associate professor in the College of Information Sciences and Technology and a member of the Huck Institutes of the Life Sciences at Penn State University, University Park, PA. Huang's research interests focus on developing robust image analysis methods that integrate algorithms with efficient, application-specific designs to solve computational problems in biomedicine and cognition. She works toward robust medical imaging softwares that aid medical doctors in accurate and reproducible diagnosis, and to better understand the basic anatomical and physiological relationships in normal and diseased states. She serves on the program committees of major conferences in medical image computing and computer vision and is an editor for several journals including the Medical Image Analysis Journal and the Computer Vision and Image Understanding journal.

We look forward to your submission and seeing you all (either virtually or in person) in another MICCAI conference! 

If you have any questions or comments, please do not hesitate to email us at: alzam...@nih.gov