[Insight-users] Call for Papers: MLMI 2010 - (Patent Disclosure ?)

[Luis Ibanez]

Hi Pingkun,


Many thanks for posting this announcement.


Are there any requirements for authors to disclose existing
or pending Patents on the methods that they will present
in this Workshop ?


For example, RSNA has a policy of disclosure that is intended
to protect attendees to their conferences and readers of their
proceedings:

http://www.rsna.org/publications/rad/PIA/policies/conflicts.html



Given the out-of-control status of software patents in the fields
of Medical Imaging and Computer Vision, it will be healthy for
participants to become aware of existing restrictions on the use
of methods and processes that are described in the scientific
literature.


This awareness if particularly important, given that not even
university research activities are exempted from the exclusive
rights of patents, since the ruling on Madey vs Duke in 2002:

http://cyber.law.harvard.edu/people/tfisher/2002Madeyedit.html

"In short, regardless of whether a particular institution or entity is
engaged in an endeavor for commercial gain, so long as the act is in
furtherance of the alleged infringer's legitimate business and is not
solely for amusement, to satisfy idle curiosity, or for strictly
philosophical inquiry, the act does not qualify for the very narrow
and strictly limited experimental use defense. Moreover, the profit or
non-profit status of the user is not determinative."



It is very expensive for universities and companies
to go through the process of adopting methods from
the scientific literature and later learning that their use
is restricted by Patents that were filed just before the
 methods were published.



Please let us know if there is a Patent Disclosure Policy
in this Workshop.



       Best Regards,



             Luis




--------------------------------------------------------------------------
On Thu, Apr 29, 2010 at 8:21 AM, Pingkun Yan <pingkun.yan at gmail.com> wrote:
> 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/
>
> ===============================================================
>
> Important dates:
>
>    Paper Submission: June 1, 2010
>    Notification of Acceptance: July 1, 2010
>    Camera Ready Version: July 15, 2010
>
> ---------------------------------------------------------------
>
> 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
> _____________________________________
> Powered by www.kitware.com
>
> Visit other Kitware open-source projects at
> http://www.kitware.com/opensource/opensource.html
>
> Kitware offers ITK Training Courses, for more information visit:
> http://www.kitware.com/products/protraining.html
>
> Please keep messages on-topic and check the ITK FAQ at:
> http://www.itk.org/Wiki/ITK_FAQ
>
> Follow this link to subscribe/unsubscribe:
> http://www.itk.org/mailman/listinfo/insight-users
>