Kenji Suzuki focuses on Artificial neural network, Artificial intelligence, Radiology, Computer-aided diagnosis and Medical imaging. The Artificial neural network study combines topics in areas such as Feature extraction, Deep learning and Convolutional neural network. His Artificial intelligence research is multidisciplinary, relying on both Computer vision and Pattern recognition.
His Radiology study combines topics in areas such as Image processing, Cancer, Computer-aided and Nuclear medicine. His Computer-aided diagnosis research is multidisciplinary, incorporating perspectives in Image segmentation and Radiography. The various areas that he examines in his Medical imaging study include Segmentation and Computed tomography.
His main research concerns Artificial intelligence, Computer vision, Artificial neural network, Pattern recognition and Radiology. His research on Artificial intelligence often connects related topics like Machine learning. His biological study spans a wide range of topics, including Deep learning, Convolutional neural network and Nodule.
Kenji Suzuki interconnects False positive paradox, Mammography and Reduction in the investigation of issues within Pattern recognition. His Radiology research includes elements of Computer aided detection and Lung. His research in Image intersects with topics in Algorithm and Connected component.
Kenji Suzuki mainly investigates Artificial intelligence, Pattern recognition, Deep learning, Artificial neural network and Intermetallic. The Artificial intelligence study combines topics in areas such as Machine learning and Reduction. As a part of the same scientific family, he mostly works in the field of Pattern recognition, focusing on Filter and, on occasion, Sensitivity, Transfer of learning, Thoracic computed tomography, Region growing and Binary image.
His Artificial neural network research incorporates themes from Segmentation, Convolution and Ground-glass opacity. His Convolutional neural network research integrates issues from False positive paradox, Training set and Feature. The study incorporates disciplines such as Computer-aided diagnosis and Decision support system in addition to Medical imaging.
Kenji Suzuki focuses on Artificial intelligence, Deep learning, Convolutional neural network, Artificial neural network and Feature. His Artificial intelligence research is multidisciplinary, relying on both Data mining, Reduction, Computer vision, Condition monitoring and Pattern recognition. His Computer vision study incorporates themes from Outlier and Generalized Procrustes analysis.
Kenji Suzuki has researched Pattern recognition in several fields, including Selection, Binary number, Heuristic and Heuristic. His Deep learning research includes themes of Probability distribution, Recurrent neural network, Imaging phantom, Nuclear medicine and Entropy. His Artificial neural network research is included under the broader classification of Machine learning.
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Linear-time connected-component labeling based on sequential local operations
Kenji Suzuki;Isao Horiba;Noboru Sugie.
Computer Vision and Image Understanding (2003)
Overview of deep learning in medical imaging
Kenji Suzuki;Kenji Suzuki.
Radiological Physics and Technology (2017)
Fast connected-component labeling
Lifeng He;Yuyan Chao;Kenji Suzuki;Kesheng Wu.
Pattern Recognition (2009)
Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography.
Kenji Suzuki;Samuel G. Armato;Feng Li;Shusuke Sone.
Medical Physics (2003)
Optimizing two-pass connected-component labeling algorithms
Kesheng Wu;Ekow Otoo;Kenji Suzuki.
Pattern Analysis and Applications (2009)
A Run-Based Two-Scan Labeling Algorithm
Lifeng He;Yuyan Chao;K. Suzuki.
IEEE Transactions on Image Processing (2008)
Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies
Ayman El-Baz;Garth M. Beache;Georgy L. Gimel'farb;Kenji Suzuki.
International Journal of Biomedical Imaging (2013)
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;Feng Li;S. Sone;K. Doi.
IEEE Transactions on Medical Imaging (2005)
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 (2006)
Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening
Hidetaka Arimura;Shigehiko Katsuragawa;Kenji Suzuki;Feng Li.
Academic Radiology (2004)
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