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ROMAN_CVML 2018 : IEEE ROMAN Computer Vision and machine learning | |||||||||||||||||
Link: http://ro-man2018.org/special-session-6-computer-vision-and-machine-learning-foundations-and-applications-in-advanced-bio-medical-applications/ | |||||||||||||||||
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Call For Papers | |||||||||||||||||
http://ro-man2018.org/special-session-6-computer-vision-and-machine-learning-foundations-and-applications-in-advanced-bio-medical-applications/
(The code for the submission: 4a64a) This workshop supported by IEEE will be held in Nanjing and Tai'an, China The special session addresses methodological issues concerning image processing and computer vision in general and bio-medical area in particular. Feature extraction, deep learning and processing big data are generic enough these days and contributions in this session may help understanding and getting foundational works that can benefit beyond bio-medical domain. Indeed, with advancement in bio-medical imaging, the amount of data generated by multimodality image techniques (e.g. ranging from Computed Tomography (CT), Magnetic Resonance Imaging (MR), Ultrasound, Single Photon Emission Computed Tomography (SPECT), and Positron Emission Tomography (PET), to Magnetic Particle Imaging, EE/MEG, Optical Microscopy and Tomography, Photoacoustic Tomography, Electron Tomography, and Atomic Force Microscopy, etc.) has grown exponentially and the nature of such data is increasingly become more complex. This poses a great challenge on how to develop new advanced imaging methods and computational models based in the computer vision for efficient data processing, analysis and modelling in clinical applications and in understanding the underlying biological process. The purpose of this workshop is to provide a diverse, but complementary, set of contributions to demonstrate new developments and applications of advanced imaging analysis in the multimodel biomedical imaging area. The ultimate goal is to promote research and development of advanced imaging analysis for multimodal biomedical images by publishing high-quality research articles and reviews in this rapidly growing interdisciplinary field. Organisers: Shui-Hua Wang (UK), Liangxiu Han (UK), Zhengchao Dong (USA) Contact: Shui-Hua Wang (shuihuawang@ieee.org) |
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