Rikiya Yamashita
Rikiya Yamashita
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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
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
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
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
Deep Learning: An Update for Radiologists
PM Cheng, E Montagnon, R Yamashita, I Pan, A Cadrin-Chênevert, ...
RadioGraphics 41 (5), 1427-1445, 2021
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer
DE Spratt, S Tang, Y Sun, HC Huang, E Chen, O Mohamad, AJ Armstrong, ...
Research Square, 2023
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