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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: Virtual colonoscopy, Neural networks, Colon, Computer-aided diagnosis, Machine learning, Cancer, Convolutional neural networks, Colorectal cancer, Algorithm development, 3D image processing, Detection and tracking algorithms
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: Artificial intelligence, Convolutional neural networks, Image quality, Chest imaging, Super resolution, Medical imaging, Neural networks, Image enhancement, Image restoration, Resolution enhancement technologies, Spatial resolution, Image resolution, Image interpolation
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: Computed tomography, Image quality, Image compression, Super resolution, Chest imaging, Signal to noise ratio, Image analysis, Image restoration, Chest, X-ray computed tomography, Medical imaging
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