[Insight-users] Call for Papers: MLMI 2010 - International Workshop on Machine Learning in Medical Imaging

[Pingkun Yan]

MLMI 2010 - International Workshop on Machine Learning in Medical Imaging
In conjunction with MICCAI 2010
 - September 20, 2010, in Beijing, China

http://miccai-mlmi.uchicago.edu/

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Important dates:

    Paper Submission: June 1, 2010
    Notification of Acceptance: July 1, 2010
    Camera Ready Version: July 15, 2010

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CALL FOR PAPERS

Machine learning plays an essential role in the medical imaging field,
including image segmentation, image registration, computer-aided
diagnosis, image fusion, image-guided therapy, image annotation and
image database retrieval. With advances in medical imaging, new
imaging modalities and methodologies such as cone-beam/multi-slice CT,
3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical
impedance tomography and diffuse optical tomography, new
machine-learning algorithms/applications are demanded in the medical
imaging field. Single-sample evidence provided by the patient’s
imaging data is often not sufficient to provide satisfactory
performance, therefore tasks in medical imaging require learning from
examples to simulate physician’s prior knowledge of the data.

Researchers are now beginning to use techniques such as modern
implementations of supervised, unsupervised, semi-supervised and
reinforcement learning, for instance using probabilistic modeling and
kernel methods. The main aim of this workshop is to help advance the
scientific research within the broad field of medical imaging and
machine learning. This workshop focuses on major trends and challenges
in this area, and work to identify new techniques and their use in
medical imaging. We are looking for original, high-quality submissions
that address innovative research and development in the analysis of
medical image data using machine learning techniques.

Topics of interests include but are not limited to:
- Machine learning (e.g., with support vector machines, statistical
methods, manifold-space-based methods, artificial neural networks)
applications to medical images with 2D, 3D and 4D data
- Medical image analysis (e.g., pattern recognition, classification,
segmentation, registration) of anatomical structures and lesions
- Multi-modality fusion (e.g., MRI, PET, CT projection X-ray, CT,
X-ray, ultrasound) for image guided interventions
- Image reconstruction for medical imaging (e.g., CT, PET, MRI, X-ray)
- Computer-aided detection/diagnosis (e.g., for lung cancer, prostate
cancer, breast cancer, colon cancer, liver cancer, acute disease,
chronic disease, osteoporosis)
- Medical image retrieval (e.g., context-based retrieval)
- Cellular image analysis (e.g., genotype, phenotype, classification,
identification, cell tracking)
- Molecular/pathologic image analysis
- Dynamic, functional, physiologic, and anatomic imaging

Organizers:
•	Pingkun Yan, Philips Research North America
•	Fei Wang, IBM Almaden Research Center
•	Kenji Suzuki, University of Chicago
•	Dinggang Shen, UNC-Chapel Hill

Program Committee
•	Vince D. Calhoun, University of New Mexico, USA
•	Heang-Ping Chan, University of Michigan Medical Center, USA
•	Marleen de Bruijne, University of Copenhagen, Denmark
•	James Duncan, Yale University, USA
•	Alejandro Frangi, Pompeu Fabra University
•	Joachim Hornegger, Friedrich-Alexander University, Germany
•	Steve B. Jiang, University of California, San Diego, USA
•	Xiaoyi Jiang, University of Münster, Germany
•	Ghassan Hamarneh, Simon Fraser University, Canada
•	Nico Karssemeijer, Radboud University Nijmegen Medical Centre, The Netherlands
•	Shuo Li, GE Healthcare, Canada
•	Marius Linguraru, National Institutes of Health, USA
•	Yoshitaka Masutani, University of Tokyo, Japan
•	Janne Nappi, Harvard Medical School, USA
•	Mads Nielsen, University of Copenhagen, Denmark
•	Sebastien Ourselin, University College London, UK
•	Daniel Rueckert, Imperial College London, UK
•	Clarisa Sanchez, University Medical Center Utrecht, The Netherlands
•	Kuntal Sengupta, MERL Research, USA
•	Akinobu Shimizu, Tokyo Univ. Agriculture and Technology, Japan
•	Dave Tahmoush, US Army Research Laboratory, USA
•	Hotaka Takizawa, University of Tsukuba, Japan
•	Xiaodong Tao, GE Global Research, USA
•	Georgia D. Tourassi, Duke University, USA
•	Zhuowen Tu, Univ. Califonia, Los Angeles, USA
•	Bram van Ginneken, Radboud University Nijmegen Medical Centre, The Netherlands
•	Guorong Wu, University of North Carolina, Chapel Hill, USA
•	Jianwu Xu, University of Chicago, USA
•	Jane You, Hong Kong Polytechnic University, China
•	Bin Zheng, University of Pittsburgh, USA
•	Guoyan Zheng, University of Bern, Switzerland
•	Kevin Zhou, Siemens Corporate Research, USA
•	Sean Zhou, Siemens Medical Solutions, USA