[image 01527] CFP: Pattern Recognition (Elsevier; IF: 3.096) - Special Issue on Machine Learning in Medical Imaging

[Kenji Suzuki]

メーリングリストの皆様

イリノイ工科大学の鈴木と申します．

ジャーナル，Pattern Recognition (Elsevier; Impact factor: 3.096)では，以下のよ
うに，「医用画像にお
ける機械学習」の研究の特集号を企画しております．この論文特集号は，今年で６回目と
なりました，MICCAI国際ワークショップ「医用画像における機械学習」
（http://mlmi2015.web.unc.edu/）と連携して企画したものですが，ワークショップ論文
に限らず，医用画像処理・解析・診断・治療に
おける機械学習の研究・開発・応用に関する論文を募集しております．

原稿執筆の案内，原稿の投稿などは，以下のサイトを御覧下さい．
http://www.journals.elsevier.com/pattern-recognition/call-for-papers/specia
l-issue-on-machine-learning-in-medical-imaging/

皆様からの論文のご投稿をお待ちしております．

鈴木賢治
イリノイ工科大学　医用画像研究所

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Call for Papers: Pattern Recognition (Elsevier; Impact factor: 3.096) -
Special Issue on Machine Learning in Medical Imaging
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Machine learning plays an essential role in the medical imaging field,
including computer-aided diagnosis, image segmentation, registration and
fusion, image-guided therapy, image annotation, and image database
retrieval. With advances in medical imaging, new imaging
modalities/methodologies and 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. Because of large variations and complexity, it
is generally difficult to derive analytic solutions or simple formula to
represent objects such as lesions and anatomies in medical images.
Therefore, tasks in medical imaging require learning from examples for
accurate representation of data and prior knowledge. Researchers are now
beginning to adapt modern machine learning (ML) and pattern recognition
(PR) techniques such as supervised, unsupervised, semi-supervised, and
deep learning to solve medical imaging related problems. Compared with
generic image analysis, medical imaging applications are specifically
characterized by the challenges of divergent inputs, the high dimensional
features versus inadequate samples, the subtle key patterns hidden by the
large individual variations, and sometimes the unknown mechanism of the
diseases.

The main scope of this special issue is to help advance the scientific
research within the broad field of machine learning in medical
imaging.This special issue will focus on major trends and challenges in
this area, and will present work aimed to identify new cutting-edge
techniques and their use in medical imaging.

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, and
extreme learning machines) with their applications to

Image analysis of anatomical structures and lesions
Computer-aided detection/diagnosis
Multi-modality fusion for diagnosis, image analysis and image guided
interventions
Medical image reconstruction
Medical image retrieval
Cellular image analysis
Molecular/pathologic image analysis
Dynamic, functional, and physiologic imaging
Authors should prepare their manuscript according to the Instructions for
Authors available from the online submission page of the Pattern
Recognition at www.elsevier.com. All the papers will be peer-reviewed
following the Pattern Recognition reviewing procedures.

Important Dates:

Submission due: December 31, 2015
Results of first round: February, 2016
Revised paper due: April, 2016
Final decision: July 31, 2016
Camera ready: August, 2016
Issue publication: October, 2016

Guest Editors:

Luping Zhou, School of Computing and Information Technology, University of
Wollongong, AUSTRALIA
lupingz ＠ uow.edu.au

Qian Wang, Med-X Research Institute, School of Biomedical Engineering,
Shanghai Jiao Tong University, CHINA
wang.qian ＠ sjtu.edu.cn

Kenji Suzuki, Department of Electrical and Computer Engineering and
Medical Imaging Research Center, Illinois Institute of Technology, USA
ksuzuki ＠ iit.edu