Artificial Intelligence in
Biomediical Imaging Lab
Artificial Intelligence in
Biomediical Imaging Lab
Institute of Innovative Research (IIR) WRHI,
Tokyo Institute of Technology
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The research interests of the Computational Intelligence in Biomedical Imaging Laboratory lie in interdisciplinary research in computer engineering and biomedicine, with its primary focuses on machine and data learning in biomedical imaging, computer-aided diagnosis and therapy, and intelligent biomedical image processing and analysis. The long-term goal of the laboratory’s research is to develop computational-intelligence technologies that learn, from data and examples, experts’ knowledge and skills in understanding images in order to make smart decisions. In the computer-aided diagnosis area, computational-intelligence systems learn to detect and diagnose lesions in biomedical images to assist physicians in their biomedical decision making.
In the image-analysis area, computational-intelligence systems learn to trace the boundary of an organ determined by an experienced physician. In the image-processing area, computational-intelligence systems learn to model the image-processing algorithm that separates bones from soft tissue in x-ray projection images. To approach our goal, we believe that creations of and innovations with sophisticated technologies, their theoretical supports, and an understanding of people’s decision-making process and of the human visual system are essential. We are making efforts to “weave” the technological sciences and biomedical sciences into a new paradigm to contribute to both computer science/engineering and biomedicine. We hope that our research will contribute to the understanding of the human visual system, and that our systems will improve people’s health and welfare, and enrich our life and society.
2020.03.26
Announcement
2020.10.09
Announcement
2020.10.09
Awards
2020.04.01
Announcement
2020.03.26
Announcement
Development and commercialization of early deep learning models that learn images directly (1994)
Development of machine learning for separating bone from soft tissue in chest radiographs (2004)
Kenji Suzuki, Ph.D. published 330 papers (including 110 peer-reviewed journal papers). He has been actively studying deep learning in medical imaging and computer-aided diagnosis in the past 25 years. His papers were cited more than 13,000 times, and his h-index is 47. He is inventor on 30 patents (including ones of earliest deep-learning patents), which were licensed to several companies and commercialized. He published 11 books and 22 book chapters, and edited 13 journal special issues. He was awarded a number of grants as PI including NIH R01 and ACS. He served as the Editor of a number of leading international journals, including Pattern Recognition and Medical Physics. He served as a referee for 91 international journals such as Science Translational Medicine (IF: 16.8) and Nature Communications (IF: 12.4), an organizer of 62 international conferences, and a program committee member of 170 international conferences. He gave 120 invited talks and keynote speeches at international conferences. He received 26 awards, including Springer-Nature EANM Most Cited Journal Paper Award 2016 and 2017 Albert Nelson Marquis Lifetime Achievement Award.
To apply for positions in our laboratory, please email Prof. Suzuki with your resume or CV, a short description of your research interests, and names/contact information for references (if available).
-Computer Scientist
If you have an M.S., Ph.D., or equivalent degree in computer science/engineering, biomedical engineering, or a similar discipline, there may be a possibility for a Computer Scientist position. This position may be suited for a person who wants to develop his/her academic career in the field of medical imaging sciences/engineering. Required skills for this position are excellent programming skills in the computer languages (C/C++, Python, and Matlab) and strong problem-solving skills. Substantial experience in 2D and 3D pattern recognition, computer vision, image analysis, and/or machine learning is desired. Research projects will involve the development of new algorithms/methods for computer-aided diagnosis or machine learning.
-Research Assistant or Research Technician
If you are currently a graduate/undergraduate student at Illinois Institute of Technology, there may be a possibility for a Research Assistant or Research Technician position. If you are seeking research experience in a medicine-related field, for example, if you have an M.D. degree and are looking for your residency, you may work on a research project in our laboratory as Research Technician or Research Professional to develop your career. There are a variety of jobs in this category: (1) collection of diagnostic images (CT, digital radiography, MRI, etc.), (2) selection of diagnostic imaging cases, (3) reconciliation of diagnostic images with radiology reports and/or pathology reports, (4) analysis of computer output on clinical cases, and so on.
-Visiting Scholar, Visiting Postdoctoral Scholar, or Visiting Professors
We accept a Visiting Scholar in a relevant research area from a university outside of the U.S., but will not accept a short-term (< six months) visitor. If you have a Ph.D., M.D., or equivalent degree in a relevant area, you may be qualified for a Visiting Scholar position. If you already have experience for several years after earning of your Ph.D. or M.D. degree, you may be qualified for a Visiting Assistant/Associate/Full Professor position.