[image 02470] CFP: Machine Learning in Medical Imaging (MLMI 2017)

[Kenji Suzuki]

MLの皆様

 
イリノイ工科大の鈴木と申します．
 

本年も，MICCAI(http://www.miccai2017.org/)と共催で，第8回国際ワークショップ「医
用画像における機械学習」
(http://mlmi2017.web.unc.edu/)を，9月10日（日）にケベック（カナダ）にて開催いたし
ます．

本ワークショップは，医用画像における機械学習の分野の国際会議の中で最も古く，最
先端かつ最良質な研究が発表される会議として知られております．毎年，医用画
像処理・解析・診断・治療における機械学習の開発・応用の先端研究が発表・議論さ
れます．近年は，深層学習の医用画像応用と研究開発が多く議論されてまいりまし
た．会議録であるLNCSに掲載された論文は引用数が大変高く，また，会議で発表された論
文は，会議後一流のジャーナルで企画されるワークショップ名と同名の特
集号への投稿に招待されます．

論文提出の締め切りが6月12日と迫っております．皆様，是非奮ってご応募頂きたく，皆
様のご応募とご参加をお待ちしております．

鈴木賢治


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

8th International Workshop on Machine Learning in Medical Imaging (MLMI
2017) 
    In conjunction with MICCAI 2017
    September 10, 2017 in Quebec City, Quebec, Canada

http://mlmi2017.web.unc.edu/

Highlights -
- Accepted papers will be invited to submit to a special issue of a
leading journal with a high impact factor
- The papers of MLMI2016 have been published in a special issue of Pattern
Recognition (Impact factor: 3.399)
- Accepted papers will be published in LNCS proceeding.
- MLMI 2017 Best Paper Award will be presented to the best overall
scientific paper.
- NVIDIA will sponsor again for the MLMI 2017 Best Paper Award!

Important Dates -
Full Paper Submission: June 12 (23:59 PST)
Notification of Acceptance: July 9
Camera-ready Version: July 23
Conference Date: September 10, 2017

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 2017)
is the eighth in a series of workshops on this topic in conjunction with
MICCAI 2017. 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of previously unpublished, contributed, and invited papers.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., deep learning, 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:
Dr. Qian Wang (Shanghai Jiao Tong University)
Dr. Yinghuan Shi (Nanjing University)
Dr. Heung-Il Suk (Korea University)
Dr. Kenji Suzuki (Illinois Institute of Technology)

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

―
Kenji Suzuki, Ph.D.
Associate Professor of Electrical and Computer Engineering
Medical Imaging Research Center
Pritzker Institute of Biomedical Science & Engineering
Illinois Institute of Technology
3440 South Dearborn Street
Chicago, IL 60616-3793
Main office: M-104, Suite 100, Technology Business Center
Second office: SH-338, Siegel Hall
Direct phone: 312-567-5232
Secretary phone: 312-567-3400
Fax: 312-567-8976
Email: ksuzuki ＠ iit.edu
Homepages: http://www.ece.iit.edu/~ksuzuki/
http://mirc1.mirc.iit.edu/people/faculty/kenji-suzuki/