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Kenji Suzuki
Kenji Suzuki
Professor of Biomedical AI, Institute of Innovative Research, Tokyo Institute of Technology
Verified email at m.titech.ac.jp - Homepage
Title
Cited by
Cited by
Year
Overview of deep learning in medical imaging
K Suzuki
Radiological physics and technology 10 (3), 257-273, 2017
11182017
Linear-time connected-component labeling based on sequential local operations
K Suzuki, I Horiba, N Sugie
Computer Vision and Image Understanding 89 (1), 1-23, 2003
6382003
Fast connected-component labeling
L He, Y Chao, K Suzuki, K Wu
Pattern recognition 42 (9), 1977-1987, 2009
4922009
Optimizing two-pass connected-component labeling algorithms
K Wu, E Otoo, K Suzuki
Pattern Analysis and Applications 12 (2), 117-135, 2009
4012009
Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies
A El-Baz, GM Beache, G Gimel'farb, K Suzuki, K Okada, A Elnakib, ...
International Journal of Biomedical Imaging 2013, 2013
3832013
Massive training artificial neural network for reduction of false positives in computerized detection of lung nodules in low-dose CT
K Suzuki, IIISG Armato, S Sone, K Doi
Medical Physics 29, p. 1322, 2002
378*2002
Artificial Neural Networks - Methodological Advances and Biomedical Applications
K Suzuki
InTech, 2011
3492011
Prognostic value of metabolic tumor burden on 18F-FDG PET in nonsurgical patients with non-small cell lung cancer
S Liao, BC Penney, K Wroblewski, H Zhang, CA Simon, R Kampalath, ...
European journal of nuclear medicine and molecular imaging 39 (1), 27-38, 2012
3252012
A run-based two-scan labeling algorithm
L He, Y Chao, K Suzuki
IEEE transactions on image processing 17 (5), 749-756, 2008
3252008
Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)
K Suzuki, H Abe, H MacMahon, K Doi
IEEE Transactions on medical imaging 25 (4), 406-416, 2006
3112006
Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network
K Suzuki, F Li, S Sone, K Doi
IEEE transactions on medical imaging 24 (9), 1138-1150, 2005
3062005
Artificial neural networks: architectures and applications
K Suzuki
Intech, 2013
2322013
Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification: MTANNs vs. CNNs
N Tajbakhsh, K Suzuki
Pattern recognition 63, 476-486, 2017
2272017
Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening1
H Arimura, S Katsuragawa, K Suzuki, F Li, J Shiraishi, S Sone, K Doi
Academic radiology 11 (6), 617-629, 2004
2252004
Quantitative computerized analysis of diffuse lung disease in high‐resolution computed tomography
Y Uchiyama, S Katsuragawa, H Abe, J Shiraishi, F Li, Q Li, CT Zhang, ...
Medical Physics 30 (9), 2440-2454, 2003
2182003
Computer-aided diagnosis
ML Giger, K Suzuki
Biomedical information technology, 359-374, 2008
2102008
Neural edge enhancer for supervised edge enhancement from noisy images
K Suzuki, I Horiba, N Sugie
IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (12), 1582 …, 2003
2052003
False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network
K Suzuki, J Shiraishi, H Abe, H MacMahon, K Doi
Academic radiology 12 (2), 191-201, 2005
1982005
Image modification and detection using massive training artificial neural networks (MTANN)
K Suzuki, K Doi
US Patent 7,545,965, 2009
1932009
Massive training artificial neural network (MTANN) for detecting abnormalities in medical images
K Suzuki, K Doi
US Patent 6,819,790, 2004
1692004
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