Author

Kenji Suzuki

Professor of Biomedical AI, Institute of Innovative Research, Tokyo Institute of Technology - Cited by 14,960 - Deep learning - Machine learning - Computer-aided diagnosis - Artificial intelligence - Medical imaging

Biography

Kenji Suzuki worked at Hitachi Medical Corp., Japan, Aichi Prefectural University, Japan, as a faculty member, and in Department of Radiology, University of Chicago, as Assistant Professor. In 2014, he joined Department of Electric and Computer Engineering and Medical Imaging Research Center, Illinois Institute of Technology, as Associate Professor (Tenured). In 2017, he was jointly appointed in World Research Hub Initiative, Institute of Innovative Research, Tokyo Institute of Technology, Japan, as Specially Appointed Professor (equivalent to Visiting Professor). He received 26 awards, including Springer-Nature EANM Most Cited Journal Paper Award 2016 and 2017 Albert Nelson Marquis Lifetime Achievement Award.
Title
Cited by
Year
A deep CNN based transfer learning method for false positive reduction
Z Shi, H Hao, M Zhao, Y Feng, L He, Y Wang, K SuzukiMultimedia Tools and Applications 78 (1), 1017-1033, 2019201
96
2019
Machine learning for medical imaging
GS Fu, Y Levin-Schwartz, QH Lin, D ZhangJournal of healthcare engineering 2019, 2019201
51
2019
Converting low-dose to higher dose 3D tomosynthesis images through machine-learning processes
K SuzukiUS Patent 10,610,182, 2020202
45
2020
Deep recurrent entropy adaptive model for system reliability monitoring
M Martínez-García, Y Zhang, K Suzuki, YD ZhangIEEE Transactions on Industrial Informatics 17 (2), 839-848, 2020202
44
2020
Radiation dose reduction in digital breast tomosynthesis (DBT) by means of deep-learning-based supervised image processing
J Liu, A Zarshenas, A Qadir, Z Wei, L Yang, L Fajardo, K SuzukiMedical Imaging 2018: Image Processing 10574, 89-97, 2018201
39
2018
Separation of bones from soft tissue in chest radiographs: Anatomy‐specific orientation‐frequency‐specific deep neural network convolution
A Zarshenas, J Liu, P Forti, K SuzukiMedical physics 46 (5), 22-2242, 2019201
23
2019
A multi-task mean teacher for semi-supervised facial affective behavior analysis
L Wang, S Wang, J Qi, K SuzukiProceedings of the IEEE/CVF International Conference on Computer Vision …, 2021202
22
2021
Measuring system entropy with a deep recurrent neural network model
M Martínez-García, Y Zhang, K Suzuki, Y Zhang2019 IEEE 17th International Conference on Industrial Informatics (INDIN) 1 …, 2019201
16
2019
Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray
M Kobayashi, J Ishioka, Y Matsuoka, Y Fukuda, Y Kohno, K Kawano, ...BMC urology 21 (1), 1-10, 2021202
11
2021
Radiation dose reduction in digital breast tomosynthesis (DBT) by means of neural network convolution (NNC) deep learning
J Liu, A Zarshenas, SA Qadir, L Yang, L Fajardo, K Suzuki14th international workshop on breast imaging (IWBI 2018) 1018, 291-300, 2018201
7
2018
AI: A New Open Access Journal for Artificial Intelligence
K SuzukiAI 1 (2), 141-142, 2020202
7
2020
Action Unit Detection by Exploiting Spatial-Temporal and Label-Wise Attention With Transformer
L Wang, J Qi, J Cheng, K SuzukiProceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022202
7
2022
Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings
HI Suk, M Liu, P Yan, C LianSpringer Nature, 2019201
6
2019
Deep neural network convolution for natural image denoising
A Zarshenas, K Suzuki2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2018201
5
2018
AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer‐aided diagnosis in medical imaging
L Hadjiiski, K Cha, HP Chan, K Drukker, L Morra, JJ Näppi, B Sahiner, ...Medical Physics 50 (2), e1-e2, 2023202
4
2023
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures: First International Workshop, UNSURE 2019, and 8th International …
H Greenspan, R Tanno, M Erdt, K Suzuki, T Arbel, C Baumgartner, ...Springer Nature, 2019201
3
2019
Development of Deep-learning Segmentation for Breast Cancer in MR Images based on Neural Network Convolution
Y Wang, Z Jin, Y Tokuda, Y Naoi, N Tomiyama, K SuzukiProceedings of the 019 8th International Conference on Computing and …, 01901
2
2019
Semantic Segmentation of Liver Tumor in Contrast-enhanced Hepatic CT by Using Deep Learning with Hessian-based Enhancer with Small Training Dataset Size
M Sato, Z Jin, K Suzuki01 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 34-37, 010
2
2021
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support
K Suzuki, M Reyes, T Syeda-Mahmood, ETH Zurich, B Glocker, R Wiest, ...Springer, 20192201
2
2019
FedAL: An Federated Active Learning Framework for Efficient Labeling in Skin Lesion Analysis
Z Deng, Y Yang, K Suzuki, Z Jin2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2022202
1
2022

Referred From: https://scholar.google.com/citations?user=MdNb8Z0AAAAJ&hl=en&oi=sra

Cited by
Citations 14960
h-index 59
i10-index 173
Recommended Journals
Open Access Journals