| Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study K Yasaka, H Akai, O Abe, S Kiryu Radiology 286 (3), 887-896, 2018 | 644 | 2018 |
| Deep learning with convolutional neural network in radiology K Yasaka, H Akai, A Kunimatsu, S Kiryu, O Abe Japanese journal of radiology 36, 257-272, 2018 | 393 | 2018 |
| Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique M Katsura, I Matsuda, M Akahane, J Sato, H Akai, K Yasaka, A Kunimatsu, ... European radiology 22, 1613-1623, 2012 | 334 | 2012 |
| Deep learning and artificial intelligence in radiology: Current applications and future directions K Yasaka, O Abe PLoS medicine 15 (11), e1002707, 2018 | 230 | 2018 |
| Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid–enhanced hepatobiliary phase MR images K Yasaka, H Akai, A Kunimatsu, O Abe, S Kiryu Radiology 287 (1), 146-155, 2018 | 207 | 2018 |
| Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction … M Katsura, I Matsuda, M Akahane, K Yasaka, S Hanaoka, H Akai, J Sato, ... Investigative radiology 48 (4), 206-212, 2013 | 169 | 2013 |
| MRI findings in posttraumatic stress disorder A Kunimatsu, K Yasaka, H Akai, N Kunimatsu, O Abe Journal of Magnetic Resonance Imaging 52 (2), 380-396, 2020 | 164 | 2020 |
| Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network K Yasaka, H Akai, A Kunimatsu, S Kiryu, O Abe European radiology 30, 3549-3557, 2020 | 126 | 2020 |
| Deep learning for staging liver fibrosis on CT: a pilot study K Yasaka, H Akai, A Kunimatsu, O Abe, S Kiryu European radiology 28, 4578-4585, 2018 | 124 | 2018 |
| Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest H Akai, K Yasaka, A Kunimatsu, M Nojima, T Kokudo, N Kokudo, ... Diagnostic and interventional imaging 99 (10), 643-651, 2018 | 92 | 2018 |
| Model-based iterative reconstruction for reduction of radiation dose in abdominopelvic CT: comparison to adaptive statistical iterative reconstruction K Yasaka, M Katsura, M Akahane, J Sato, I Matsuda, K Ohtomo Springerplus 2, 1-9, 2013 | 91 | 2013 |
| Deep learning to differentiate parkinsonian disorders separately using single midsagittal MR imaging: a proof of concept study S Kiryu, K Yasaka, H Akai, Y Nakata, Y Sugomori, S Hara, M Seo, O Abe, ... European radiology 29, 6891-6899, 2019 | 68 | 2019 |
| Precision of quantitative computed tomography texture analysis using image filtering: a phantom study for scanner variability K Yasaka, H Akai, D Mackin, E Moros, K Ohtomo, S Kiryu Medicine 96 (21), e6993, 2017 | 63 | 2017 |
| Comparison of pure and hybrid iterative reconstruction techniques with conventional filtered back projection: image quality assessment in the cervicothoracic region M Katsura, J Sato, M Akahane, I Matsuda, M Ishida, K Yasaka, ... European journal of radiology 82 (2), 356-360, 2013 | 57 | 2013 |
| Machine learning-based texture analysis of contrast-enhanced MR imaging to differentiate between glioblastoma and primary central nervous system lymphoma A Kunimatsu, N Kunimatsu, K Yasaka, H Akai, K Kamiya, T Watadani, ... Magnetic Resonance in Medical Sciences 18 (1), 44-52, 2019 | 54 | 2019 |
| Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis S Naganawa, K Enooku, R Tateishi, H Akai, K Yasaka, J Shibahara, ... European radiology 28, 3050-3058, 2018 | 52 | 2018 |
| The feasibility of Forward-projected model-based Iterative Reconstruction SoluTion (FIRST) for coronary 320-row computed tomography angiography: a pilot study E Maeda, N Tomizawa, S Kanno, K Yasaka, T Kubo, K Ino, R Torigoe, ... Journal of cardiovascular computed tomography 11 (1), 40-45, 2017 | 51 | 2017 |
| Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator S Kiryu, H Akai, M Nojima, K Hasegawa, H Shinkawa, N Kokudo, ... Scientific reports 7 (1), 12689, 2017 | 49 | 2017 |
| Clinical impact of deep learning reconstruction in MRI S Kiryu, H Akai, K Yasaka, T Tajima, A Kunimatsu, N Yoshioka, ... Radiographics 43 (6), e220133, 2023 | 46 | 2023 |
| Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors K Yasaka, H Akai, M Nojima, A Shinozaki-Ushiku, M Fukayama, ... European Journal of Radiology 92, 84-92, 2017 | 46 | 2017 |
Osamu AbeThe University of TokyoAdresse e-mail validée de umin.ac.jp
