CONFERENCE PROCEEDINGS
Advanced Search
Dr. Kensuke Umehara
Senior Researcher at QST
SPIE Involvement:
Author
Area of Expertise:
deep learning , super-resolution , computer-aided detection , computer vision , machine learning , medical image analysis
Websites:
Personal Website
Profile Summary
Dr. Umehara received his B.S., M.S., and Ph.D. degrees from Osaka University, Japan, in 2013, 2015, and 2018, respectively. He is currently working as a tenured Senior Researcher in National Institutes for Quantum Science and Technology (QST), Chiba, Japan. His research interests include artificial intelligence, machine learning, and super-resolution in medical imaging. He received an Honorable Mention award from the SPIE Medical Imaging and a Cum Laude award and two Certificate of Merit awards from the Annual Meetings of Radiological Society of North America (RSNA). He is a member of RSNA, IEEE, and SPIE.
Publications (3)
Proceedings Article | 3 March 2017 Presentation + Paper
Deep ensemble learning of virtual endoluminal views for polyp detection in CT colonography
Proceedings Volume 10134, 101340G (2017) https://doi.org/10.1117/12.2255606
KEYWORDS: Cancer, Convolutional neural networks, Detection and tracking algorithms, Neural networks, Colorectal cancer, Machine learning, Colon, Algorithm development, Computer-aided diagnosis, 3D image processing, Virtual colonoscopy
Proceedings Article | 24 February 2017 Paper
Super-resolution convolutional neural network for the improvement of the image quality of magnified images in chest radiographs
Kensuke Umehara, Junko Ota, Naoki Ishimaru, Shunsuke Ohno, Kentaro Okamoto, Takanori Suzuki, Naoki Shirai, Takayuki Ishida
Proceedings Volume 10133, 101331P (2017) https://doi.org/10.1117/12.2249969
KEYWORDS: Super resolution, Convolutional neural networks, Image restoration, Image resolution, Medical imaging, Image quality, Neural networks, Image enhancement, Artificial intelligence, Spatial resolution, Chest imaging, Image interpolation, Resolution enhancement technologies
Proceedings Article | 24 February 2017 Paper
Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography
Junko Ota, Kensuke Umehara, Naoki Ishimaru, Shunsuke Ohno, Kentaro Okamoto, Takanori Suzuki, Naoki Shirai, Takayuki Ishida
Proceedings Volume 10133, 101331S (2017) https://doi.org/10.1117/12.2253582
KEYWORDS: Signal to noise ratio, Super resolution, Chest, Image compression, X-ray computed tomography, Image restoration, Image analysis, Medical imaging, Image quality, Computed tomography, Chest imaging
SIGN IN TO:
  • View contact details
UPDATE YOUR PROFILE
Is this your profile? Update it now.
Sign into your SPIE.org account
Don’t have a profile and want one?
Create an account on SPIE.org
Advertisement
Advertisement
Back to Top