Elsevier

Pattern Recognition

Volume 63, March 2017, Pages 465-467
Pattern Recognition

Machine learning in medical imaging

https://doi.org/10.1016/j.patcog.2016.10.020Get rights and content

Section snippets

1. Introduction

There is no doubt that medical and biological imaging has become indispensable in diagnosis and therapy of diseases. With advances in new biomedical imaging modalities and methodologies such as diffusion-weighted magnetic resonance imaging (MRI), positron-emission tomography (PET)-CT/MRI, tomosynthesis, cone-beam CT, 3D ultrasound imaging, electrical impedance tomography, and diffuse optical tomography, the imaging information available for clinical decision making has been exploding. To take

2. Overview of accepted articles

The special issue covers a wide spectrum of machine learning techniques and their biomedical applications with different imaging modalities.

Acknowledgment

We would like to thank all the authors for their outstanding contributions to this special issue and to all the reviewers for their high-quality reviews and constructive suggestions. We hope that this special issue will inspire further ideas for creative research, advance the field of machine learning in medical imaging, and facilitate the translation of the research from bench to bedside.

Kenji Suzuki, Ph.D. (Nagoya University, Japan) worked at Hitachi Medical Corp, Japan, Aichi Prefectural University, Japan, and in Department of Radiology, University of Chicago, as Assistant Professor. In 2014, he joined Department of Electric and Computer Engineering and Medical Imaging Research Center, Illinois Institute of Technology, as Associate Professor. He published more than 300 papers including 107 peer-reviewed journal papers. His papers were cited more than 7000 times by other

References (6)

  • L. Zhou, L. Wang, Q. Wang, Y.Shi, Machine learning in medical imaging, in: Proceedings of the 6th International...
  • F. Wang, P. Yan, K. Suzuki, D. Shen, Machine learning in medical imaging, in: Proceedings of the First International...
  • K. Suzuki, F. Wang, D. Shen, P. Yan, Machine learning in medical imaging, in: Proceedings of the 2nd International...
There are more references available in the full text version of this article.

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Kenji Suzuki, Ph.D. (Nagoya University, Japan) worked at Hitachi Medical Corp, Japan, Aichi Prefectural University, Japan, and in Department of Radiology, University of Chicago, as Assistant Professor. In 2014, he joined Department of Electric and Computer Engineering and Medical Imaging Research Center, Illinois Institute of Technology, as Associate Professor. He published more than 300 papers including 107 peer-reviewed journal papers. His papers were cited more than 7000 times by other researchers. His h-index is 37. He is inventor on 30 patents (including 12 granted patents), which were licensed to several companies and commercialized. He published 10 books and 22 book chapters, and edited 12 journal special issues. He was awarded 25 Grants as P.I. including NIH R01 and ACS Grants. He served as the Editor or Guest Editor of a number of leading international journals, including Medical Physics, Neurocomputing and Academic Radiology. He served as a reviewer for 80 international journals, an organizer of 18 international conferences, and a program committee member of 140 international conferences. He received 25 awards for his research and education, including the Best Journal Paper Awards from IEICE and EJNMMI

Luping Zhou is a Senior Lecturer in School of Computing and Information Technology, University of Wollongong, Australia. She obtained her Ph.D., M.Sc, and B.E from Australian National University, National University of Singapore and Southeast University, China, respectively. After PhD, she worked as a postdoctoral research fellow in University of North Carolina at Chapel Hill, USA and then a staff research scientist in Australian e-Health Research Center, CSIRO. Zhou joined University of Wollongong in 2012 with Vice Chancellor Research Fellowship. She received the Discovery Early Career Researcher Award (DECRA) from Australia Research Council in 2015. Zhou has broad research interest in medical image analysis, machine learning and computer vision. Her work has been published on premier journals and conferences in the related fields. Zhou is the General Chair of the international workshop MLMI (Machine Learning in Medical Imaging) 2015 in Munich Germany, and MLMI 2014 in Boston, US. Zhou has served 20+ international journals, conferences and workshops as chair, TPC member or reviewer. Zhou is a senior member of IEEE.

Qian Wang, Ph.D., completed both his bachelor and master degrees in Department of Electronic Engineering, Shanghai Jiao Tong University (SJTU) in 2006 and 2009, respectively. He then completed the doctorate degree of computer science in the University of North Carolina at Chapel Hill (UNC), United States in 2013. Later he joined the Med-X Research Institute, School of Biomedical Engineering, SJTU, and founded the Medical Image Computing (MIC) Lab (http://mic.sjtu.edu.cn) there. His research interest includes image registration, image segmentation, and imaging based translational medical study. He has published more than 60 peer-reviewed journal and conference papers, which have been cited for more than 600 times. His work is now sponsored by the Grants from National Natural Science Foundation of China (NSFC) and Science and Technology Commission of Shanghai Municipality (STCSM).

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