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Hisaichi Shibata
Hisaichi Shibata
The University of Tokyo Hospital
Verified email at umin.ac.jp - Homepage
Title
Cited by
Cited by
Year
Dynamic mode decomposition using a Kalman filter for parameter estimation
T Nonomura, H Shibata, R Takaki
AIP Advances 8 (10), 2018
632018
Extended-Kalman-filter-based dynamic mode decomposition for simultaneous system identification and denoising
T Nonomura, H Shibata, R Takaki
PloS one 14 (2), e0209836, 2019
582019
Stable, non-dissipative, and conservative flux-reconstruction schemes in split forms
Y Abe, I Morinaka, T Haga, T Nonomura, H Shibata, K Miyaji
Journal of Computational Physics 353, 193-227, 2018
452018
Performance prediction of electrohydrodynamic thrusters by the perturbation method
H Shibata, Y Watanabe, K Suzuki
Physics of Plasmas 23 (5), 2016
112016
Global stability analysis method to numerically predict precursor of breakdown voltage
H Shibata, Y Ohmichi, Y Watanabe, K Suzuki
Plasma Sources Science and Technology 24 (5), 055014, 2015
92015
Multichannel three-dimensional fully convolutional residual network-based focal liver lesion detection and classification in Gd-EOB-DTPA-enhanced MRI
T Takenaga, S Hanaoka, Y Nomura, T Nakao, H Shibata, S Miki, ...
International Journal of Computer Assisted Radiology and Surgery 16, 1527-1536, 2021
82021
Playing the werewolf game with artificial intelligence for language understanding
H Shibata, S Miki, Y Nakamura
arXiv preprint arXiv:2302.10646, 2023
72023
A novel method to predict current voltage characteristics of positive corona discharges based on a perturbation technique. I. Local analysis
H Shibata, R Takaki
Aip Advances 7 (11), 2017
52017
Numerical study on fundamental characteristics of electro-hydrodynamic thruster for mobility in planetary atmosphere
H Shibata, Y Watanabe, R Yano, K Suzuki
Transactions of the Japan Society for Aeronautical and Space Sciences …, 2014
52014
Versatile anomaly detection method for medical images with semi-supervised flow-based generative models
H Shibata, S Hanaoka, Y Nomura, T Nakao, I Sato, D Sato, N Hayashi, ...
International Journal of Computer Assisted Radiology and Surgery 16 (12 …, 2021
42021
Preliminary study of generalized semiautomatic segmentation for 3D voxel labeling of lesions based on deep learning
Y Nomura, S Hanaoka, T Takenaga, T Nakao, H Shibata, S Miki, ...
International Journal of Computer Assisted Radiology and Surgery 16, 1901-1913, 2021
42021
On the simulation of ultra-sparse-view and ultra-low-dose computed tomography with maximum a posteriori reconstruction using a progressive flow-based deep generative model
H Shibata, S Hanaoka, Y Nomura, T Nakao, T Takenaga, N Hayashi, ...
Tomography 8 (5), 2129-2152, 2022
32022
X2CT-FLOW: Maximum a posteriori reconstruction using a progressive flow-based deep generative model for ultra sparse-view computed tomography in ultra low-dose protocols
H Shibata, S Hanaoka, Y Nomura, T Nakao, T Takenaga, N Hayashi, ...
arXiv preprint arXiv:2104.04179, 2021
32021
階層型等間隔直交構造格子を用いた高速・高精度乱流解析プログラムの開発
高木, 亮治, 河合, 宗司, 福島
宇宙航空研究開発機構特別資料: 第 51 回流体力学講演会/第 37 回航空宇宙数値シミ …, 2020
32020
Practical Medical Image Generation with Provable Privacy Protection based on Denoising Diffusion Probabilistic Models for High-resolution Volumetric Images
H Shibata, S Hanaoka, T Nakao, T Kikuchi, Y Nakamura, Y Nomura, ...
Authorea Preprints, 2023
22023
Local differential privacy image generation using flow-based deep generative models
H Shibata, S Hanaoka, Y Cao, M Yoshikawa, T Takenaga, Y Nomura, ...
Applied Sciences 13 (18), 10132, 2023
22023
A versatile anomaly detection method for medical images with a flow-based generative model in semi-supervision setting
H Shibata, S Hanaoka, Y Nomura, T Nakao, I Sato, D Sato, N Hayashi, ...
arXiv preprint arXiv:2001.07847, 2020
22020
Anomaly detection in chest radiographs with a weakly supervised flow-based deep learning method
H Shibata, S Hanaoka, Y Nomura, T Nakao, I Sato, N Hayashi, O Abe
arXiv preprint arXiv:2001.07847, 2020
22020
X2 CT-FLOW: Reconstruction of multiple volumetric chest computed tomography images with different likelihoods from a uni-or biplanar chest X-ray image using a flow-based …
H Shibata, S Hanaoka, Y Nomura, T Nakao, T Takenaga, N Hayashi, ...
arXiv preprint arXiv:2104.04179, 2021
12021
Theory of Hallucinations based on Equivariance
H Shibata
arXiv preprint arXiv:2312.14504, 2023
2023
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