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Publication List - Kenji Suzuki 2017 (27 / 382 entries)
Journal Paper
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Kenji Suzuki.
Overview of deep learning in medical imaging,
Radiological Physics and Technology,
Vol. 10,
No. 3,
pp. 257-273,
Sept. 2017.
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Kenji Suzuki.
Survey of Deep Learning Applications to Medical Image Analysis,
Medical Imaging Technology,
Vol. 35,
No. 4,
pp. 212-226,
Sept. 2017.
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平野靖,
伊藤貴佳,
橋本典明,
木戸尚治,
Kenji Suzuki.
胸腹部コンピューター支援診断におけるMTANN深層学習,
Medical Imaging Technology,
vol. 35,
no. 4,
pp. 194-199,
Sept. 2017.
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Kenji SUZUKI.
Machine Learning in Medical Imaging Before and After Introduction of Deep Learning,
Medical Imaging and Information Sciences,
Vol. 34,
No. 2,
pp. 14-24,
June 2017.
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Chen, Y.,
Chan, A.B.,
Lin, Z.,
Wang, G.,
Kenji Suzuki.
Efficient tree-structured SfM by RANSAC generalized Procrustes analysis,
Computer Vision and Image Understanding,
Vol. 157,
pp. 179-189,
Apr. 2017.
Official location
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Ayman El-Baz,
Georgy Gimel’farb,
Kenji Suzuki.
Special issue: Machine Learning Applications in Medical Image Analysis,
Computational and Mathematical Methods in Medicine,
Hindawi,
Vol. 2017,
Apr. 2017.
Official location
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Kenji Suzuki,
Luping Zhou,
Qian Wang.
Special issue: Machine Learning in Medical Imaging,
Pattern Recognition,
Elsevier,
Vol. 63,
pp. 740,
Mar. 2017.
Official location
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Huynh, H.T.,
Le-Trong, N.,
Bao, P.T.,
Oto, A.,
Kenji Suzuki.
Fully automated MR liver volumetry using watershed segmentation coupled with active contouring,
International Journal of Computer Assisted Radiology and Surgery,
Vol. 12,
No. 2,
pp. 235-243,
Feb. 2017.
Official location
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Tajbakhsh, N.,
Kenji Suzuki.
Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs,
Pattern Recognition,
Vol. 63,
pp. 476-486,
2017.
Official location
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He, L.,
Zhao, X.,
Yao, B.,
Yang, Y.,
Chao, Y.,
Shi, Z.,
Kenji Suzuki.
A combinational algorithm for connected-component labeling and Euler number computing,
Journal of Real-Time Image Processing,
Vol. 13,
No. 4,
pp. 703-712,
2017.
Official location
Book
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Kenji Suzuki,
Liu J.,
Zarshenas A.,
Higaki T.,
Fukumoto W.,
Awai, K..
Neural network convolution (NNC) for converting ultra-low-dose to “virtual” high-dose CT images,
Lecture Notes in Computer Science (Lecture Notes in Computer Science),
Vol. 10541,
pp. 334-343,
Sept. 2017.
Official location
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Kenji Suzuki.
Computer-aided detection of lung cancer,
Image-Based Computer-Assisted Radiation Therapy,
Springer,
pp. 9-40,
Jan. 2017.
Official location
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Wang Qian,
Shi Yinghuan,
Suk Heung-Il,
Kenji Suzuki.
Machine Learning in Medical Imaging (Lecture Notes in Computer Science 10541),
Lecture Notes in Computer Science,
Springer Cham,
vol. 10541,
pp. 391,
2017.
International Conference (Reviewed)
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Liu J.,
Zarshenas A.,
Wei Z.,
Yang L.,
Fajardo L. L.,
Suzuki K..
Virtual High-Dose (VHD) Technology: Radiation Dose Reduction in Digital Breast Tomosynthesis (DBT) by Means of Supervised Deep-Learning Image Processing (DLIP),
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Suzuki K.,
Zarshenas A.,
Liu J.,
Zhao Y.,
Luo Y..
What Was Changed in Machine Learning (ML) in Medical Image Analysis After the Introduction of Deep Learning?,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Liu J.,
Zarshenas A.,
Wei Z.,
Yang L.,
Fajardo L. L.,
Suzuki K..
Computer-Based Interactive Demonstration and Comparative Study: Virtual Full-Dose (VFD) Digital Breast Tomosynthesis (DBT) Images Derived From Reduced-Dose Acquisitions versus Clinical Full-Dose DBT Images,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Tajbakhsh N.,
Zarshenas A.,
Liu J.,
Suzuki K..
Two Deep-Learning Models for Lung Nodule Detection and Classification in CT: Convolutional Neural Network (CNN) vs Neural Network Convolution (NNC),
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Zarshenas A.,
Zhao Y.,
Liu J.,
Higaki T.,
Awai K.,
Suzuki K..
Radiation Dose Reduction in Thin-Slice Chest CT at a Micro-Dose (mD) Level by Means of 3D Deep Neural Network Convolution (NNC),
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Ito T.,
Hirano Y.,
Suzuki K.,
Kido S..
First-Reader Computerized System for Distinction between Malignant and Benign Nodules on Thoracic CT Images By Means of End-To-End Deep Learning: Convolutional Neural Network (CNN) and Neural Network Convolution (NNC) Approaches,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Fukumoto W.,
Suzuki K.,
Higaki T.,
Zhao Y.,
Zarshenas A.,
Awai K..
Detection of Solid Pulmonary Nodules in Micro-Dose CT (mDCT) with “Virtual” Higher-Dose (vHD) CT Technology: An Observer Performance Study,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Zarshenas A.,
Patel J. V.,
Liu J.,
Forti P.,
Suzuki K..
Virtual Dual-Energy (VDE) Imaging: Separation of Bones from Soft Tissue in Chest Radiographs (CXRs) by Means of Anatomy-Specific (AS) Orientation-Frequency-Specific (OFS) Deep Neural Network Convolution (NNC),
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Tajbakhsh N.,
Zarshenas A.,
Liu J.,
Suzuki K..
Investigating the Depth of Convolutional Neural Networks (CNNs) in Computer-aided Detection and Classification of Focal Lesions: Lung Nodules in Thoracic CT and Colorectal Polyps in CT Colonography,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Zarshenas A.,
Liu J.,
Suzuki K..
Highly Efficient Biomarker Selection (BS) Based on Novel Binary Coordinate Accent (BCA) for Machine Learning with a Large Dataset in Radiomics,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
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Tajbakhsh N.,
Zarshenas A.,
Liu J.,
Suzuki K..
How Deep Should We Go with Deep Learning in Medical Image Analysis?,
Scientific Assembly and Annual Meeting of Radiological Society of North America (RSNA),
Nov. 2017.
Other Publication
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Kenji Suzuki.
Deep Learning Applications, Research and Development in Medical Imaging: Introduction,
Vol. 35,
No. 4,
pp. 177-179,
Sept. 2017.
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El-Baz A.,
Gimel'farb G.,
Kenji Suzuki.
Machine Learning Applications in Medical Image Analysis,
Computational and Mathematical Methods in Medicine,
Vol. 2017,
Apr. 2017.
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Kenji Suzuki,
Zhou L.,
Wang Q..
Machine learning in medical imaging,
Pattern Recognition,
Vol. 63,
Issue C,
pp. 465–467,
Mar. 2017.
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