| Convolutional neural networks: an overview and application in radiology R Yamashita, M Nishio, RKG Do, K Togashi Insights into imaging 9 (4), 611-629, 2018 | 4137 | 2018 |
| Sorafenib for Advanced and Refractory Desmoid Tumors MM Gounder, MR Mahoney, BA Van Tine, V Ravi, S Attia, HA Deshpande, ... New England Journal of Medicine 379 (25), 2417-2428, 2018 | 372 | 2018 |
| Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study R Yamashita, J Long, T Longacre, L Peng, G Berry, B Martin, J Higgins, ... The Lancet Oncology 22 (1), 132-141, 2021 | 226 | 2021 |
| 18F-FDG PET/CT for Monitoring of Ipilimumab Therapy in Patients with Metastatic Melanoma K Ito, R Teng, H Schöder, JL Humm, A Ni, L Michaud, R Nakajima, ... Journal of Nuclear Medicine 60 (3), 335-341, 2019 | 137 | 2019 |
| Deep Learning: An Update for Radiologists PM Cheng, E Montagnon, R Yamashita, I Pan, A Cadrin-Chênevert, ... RadioGraphics 41 (5), 1427-1445, 2021 | 95 | 2021 |
| Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation JS Golia Pernicka, J Gagniere, J Chakraborty, R Yamashita, L Nardo, ... Abdominal Radiology 44 (11), 3755-3763, 2019 | 82 | 2019 |
| Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset S Tang, A Ghorbani, R Yamashita, S Rehman, JA Dunnmon, J Zou, ... Scientific Reports 11 (1), 1-9, 2021 | 74 | 2021 |
| Diffusion-weighted magnetic resonance imaging in autoimmune pancreatitis T Taniguchi, H Kobayashi, K Nishikawa, E Iida, Y Michigami, E Morimoto, ... Japanese journal of radiology 27 (3), 138-142, 2009 | 72 | 2009 |
| Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation R Yamashita, T Perrin, J Chakraborty, JF Chou, N Horvat, MA Koszalka, ... European Radiology 30 (1), 195-205, 2020 | 70 | 2020 |
| Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging T Perrin, A Midya, R Yamashita, J Chakraborty, T Saidon, WR Jarnagin, ... Abdominal Radiology 43 (12), 3271-3278, 2018 | 48 | 2018 |
| Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation R Yamashita, J Long, S Banda, J Shen, DL Rubin IEEE Transactions on Medical Imaging 40 (12), 3945-3954, 2021 | 45 | 2021 |
| Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images R Yamashita, J Long, A Saleem, DL Rubin, J Shen Scientific reports 11 (1), 1-14, 2021 | 38 | 2021 |
| Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease T Nishi, R Yamashita, S Imura, K Tateishi, H Kitahara, Y Kobayashi, ... International Journal of Cardiology 333, 55-59, 2021 | 33 | 2021 |
| Multiplexed imaging analysis of the tumor-immune microenvironment reveals predictors of outcome in triple-negative breast cancer A Patwa, R Yamashita, J Long, T Risom, M Angelo, L Keren, DL Rubin Communications Biology 4 (1), 1-14, 2021 | 31 | 2021 |
| Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study R Yamashita, A Mittendorf, Z Zhu, KJ Fowler, CS Santillan, CB Sirlin, ... Abdominal Radiology 45 (1), 24-35, 2020 | 29 | 2020 |
| F-18 fluorodeoxyglucose uptake in a solid pseudopapillary tumor of the pancreas mimicking malignancy K Shimada, Y Nakamoto, H Isoda, Y Maetani, R Yamashita, S Arizono, ... Clinical nuclear medicine 33 (11), 766-768, 2008 | 26 | 2008 |
| Development and Use of Natural Language Processing for Identification of Distant Cancer Recurrence and Sites of Distant Recurrence Using Unstructured Electronic Health Record Data YH Karimi, DW Blayney, AW Kurian, J Shen, R Yamashita, D Rubin, ... JCO Clinical Cancer Informatics 5, 469-478, 2021 | 24 | 2021 |
| Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study H Nakai, K Fujimoto, R Yamashita, T Sato, Y Someya, K Taura, H Isoda, ... Japanese journal of radiology 39 (7), 690-702, 2021 | 22 | 2021 |
| Quantitative and Qualitative Evaluation of Convolutional Neural Networks with a Deeper U-Net for Sparse-View Computed Tomography Reconstruction H Nakai, M Nishio, R Yamashita, A Ono, KK Nakao, K Fujimoto, K Togashi Academic radiology 27 (4), 563-574, 2020 | 21 | 2020 |
| Non‐contrast‐enhanced MR portography with balanced steady‐state free‐precession sequence and time‐spatial labeling inversion pulses: Comparison of imaging with flow‐in and flow … A Furuta, H Isoda, R Yamashita, T Ohno, S Kawahara, H Shimizu, ... Journal of Magnetic Resonance Imaging 40 (3), 583-587, 2014 | 16 | 2014 |
