[image 00259] Machine Learning in Medical Imaging (MLMI) 2013, Nagoya, Japan

[Kenji Suzuki]

MLの皆様

 
シカゴ大学の鈴木と申します．
 

本年も，MICCAI（http://www.miccai2013.org/）と共催で，第4回国際ワークショップ「医
用画像における機械学習」を，9月
22日（日）に名古屋大学にて開催いたします．プログラムなどの詳細は，以下のホームペー
ジをご参照下さい．
 

http://mlmi2013.web.unc.edu/
http://mlmi2013.web.unc.edu/program/

医用画像処理・解析・診断における機械学習の開発・応用の先端研究が発表・議論さ
れる予定です．皆様のご参加をお待ちしております．

なお，本ワークショップへの参加登録は，以下のページより行って下さい．
http://www.miccai2013.org/registration.html

鈴木賢治


===============================================================

MLMI 2013 - 4th International Workshop on Machine Learning in Medical
Imaging
    In conjunction with MICCAI 2013
    - September 22nd, Nagoya, Japan

http://mlmi2013.web.unc.edu/

Highlights:

- Dr. Naonori Ueda, Director of Machine Learning & Data Science Center,
NTT Communication Science (CS) Laboratories will give a keynote talk,
"Basics of Bayesian Modeling in Machine Learning"
- The Best Paper Award will be presented to the best overall scientific
paper
- Accepted papers will be invited to submit to a special issue on Machine
Learning in Medical Imaging of International Journal of Computerized
Medical Imaging and Graphics
- Proceedings will be published as a volume in the Springer Lecture Notes
in Computer Science (LNCS) series (EI, ISTP indexed)

---------------------------------------------------------------

Overview -- Machine learning plays an essential role in the medical
imaging field, including computer-aided diagnosis, image segmentation,
image registration, image fusion, image-guided therapy, image annotation
and image database retrieval. Machine Learning in Medical Imaging (MLMI
2013) is the fourth in a series of workshops on this topic in conjunction
with MICCAI 2013. This workshop focuses on major trends and challenges in
this area, and it presents work aimed to identify new cutting-edge
techniques and their use in medical imaging.

Objective -- Our goal is to help advance the scientific research within
the broad field of machine learning in medical imaging. The technical
program will consist of previously unpublished, contributed, and invited
papers. We are looking for original, high-quality submissions on
innovative research and development in the analysis of medical image data
using machine learning techniques.

Topics -- Topics of interests include but are not limited to machine
learning methods (e.g., support vector machines, statistical methods,
manifold-space-based methods, artificial neural networks, extreme learning
machines) with their applications to the following areas:
- Medical image analysis (e.g., pattern recognition, classification,
segmentation, registration) of anatomical structures and lesions
- Computer-aided detection/diagnosis (e.g., for lung cancer, prostate
cancer, breast cancer, colon cancer, brain diseases, liver cancer, acute
disease, chronic disease, osteoporosis)
- Multi-modality fusion (e.g., MRI/PET, PET/CT, projection X-ray/CT,
X-ray/ultrasound) for diagnosis, image analysis and image guided
interventions
- Image reconstruction (e.g., expectation maximization (EM) algorithm,
statistical methods, iterative reconstruction) for medical imaging (e.g.,
CT, PET, MRI, X-ray)
- Image retrieval (e.g., context-based retrieval, lesion similarity)
- Cellular image analysis (e.g., genotype, phenotype, classification,
identification, cell tracking)
- Molecular/pathologic image analysis (e.g., PET, digital pathology)
- Dynamic, functional, physiologic, and anatomic imaging

Workshop Organizers:
Prof. Dinggang Shen, University of North Carolina at Chapel Hill
Prof. Pingkun Yan, Xi’an Institute of Optics and Precision Mechanics, China
Prof. Kenji Suzuki, The University of Chicago
Dr. Fei Wang, IBM Almaden Research Center

Program Co-Chairs:
Dr. Guorong Wu, University of North Carolina at Chapel Hill
Prof. Daoqiang Zhang, Nanjing University of Aeronautics and Astronautics

Program Committee:
Amir Tahmasebi, Philips, USA
Anant Madabhushi, Rutgers – State University of New Jersey, USA
Bram van Ginneken, Radboud University Nijmegen Medical Centre, Netherlands
Brent Munsell, Claflin University, USA
Chong Yaw Wee, University of North Carolina, Chapel Hill, USA
Clarisa Sanchez, Radboud University Nijmegen Medical Center, Netherlands
Dajiang Zhu, University of Georgia, USA
Daniel Rueckert, Imperial College London, UK
Edward Herskovits, The University of Maryland, Baltimore County, USA
Emanuele Olivetti, Fondazione Bruno Kessler, Italy
Feiping Nie, University of Texas at Arlington, USA
Feng Shi, University of North Carolina, Chapel Hill, USA
Ghassan Hamarneh, Simon Fraser University, Canada
Greg Slabaugh, City University London, UK
Guangzhi Cao, GE Healthcare, USA
Guo Cao, Nanjing University of Science and Technology, China
Heang-Ping Chan, University of Michigan Medical Center, USA
Heng Huang, University of Texas at Arlington, USA
Hidetaka Arimura, Kyusyu University, Japan
Hongtu Zhu, University of North Carolina, Chapel Hill, USA
Hotaka Takizawa, University of Tsukuba, Japan
Ipek Oguz, The University of Iowa, USA
Jianming Liang, Arizona State University, USA
Jieping Ye, Arizona State University, USA
Jing Liu, UCSF, UCA
Jun Shi, Shanghai University, China
Junzhou Huang, University of Texas, USA
Kazunori Okada, San Francisco State University, USA
Kevin Zhou, Siemens Corporate Research, USA
Kilian Pohl, University of Pennsylvania, USA
Kongkuo Lu, Philips, USA
Le Lu, NIH, USA
Li Shen, Indiana University School of Medicine, USA
Liang Zhan, UCLA, USA
Lin Yang, University of Kentucky, USA
Luping Zhou, CSIRO, Australia
Marc Niethammer, University of North Carolina, Chapel Hill, USA
Marius Linguraru, National Institutes of Health, USA
Marleen de Bruijne, University of Copenhagen, Denmark
Mert Sabuncu, MGH, Harvard Medical School, USA
Min Shin, University of North Carolina, Charlotte, USA
Minjeong Kim, University of North Carolina, Chapel Hill, USA
Nico Karssemeijer, Radboud University Nijmegen Medical Centre, Netherlands
Qian Wang, University of North Carolina, Chapel Hill, USA
Ron Summers, NIH, USA
Rong Chen, The University of Maryland, Baltimore County, USA
Ruijiang Li, Standford University, USA
Sean Zhou, Siemens Medical Solutions, USA
Shaoting Zhang, Rutgers University, USA
Shuo Li, GE Healthcare, Canada
Siamak Ardekani, JHU, USA
Ting Chen, Ventana, USA
Tolga Tasdizen, University of Utah, USA
Weidong (Tom) Cai, The University of Sydney, Australia
Xiangrong Zhou, Gifu University, Japan
Xiaoyi Jiang, University of Muenster, Germany
Yalin Wang, Arizona State University, USA
Yang Li, Allen Institute for Brain Science, USA
Yiqiang Zhan, Siemens Medical Solutions, US
Yong Fan, Chinese Academy of Sciences, China
Yong He, Beijing Normal University, China
Yoshitaka Masutani, University of Tokyo, Japan