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鈴木賢治 2017年 研究業績一覧 (27件 / 383件)
論文
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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,
鈴木賢治.
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.
公式リンク
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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.
公式リンク
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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.
公式リンク
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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.
公式リンク
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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.
公式リンク
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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.
公式リンク
著書
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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.
公式リンク
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Kenji Suzuki.
Computer-aided detection of lung cancer,
Image-Based Computer-Assisted Radiation Therapy,
Springer,
pp. 9-40,
Jan. 2017.
公式リンク
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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.
国際会議発表 (査読有り)
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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.
その他の論文・著書など
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鈴木賢治.
特集/医用画像工学分野におけるディープラーニング応用と研究開発 —序文—,
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.
-
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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