| Application of super-resolution convolutional neural network for enhancing image resolution in chest CT K Umehara, J Ota, T Ishida Journal of digital imaging 31, 441-450, 2018 | 169 | 2018 |
| Super-resolution convolutional neural network for the improvement of the image quality of magnified images in chest radiographs K Umehara, J Ota, N Ishimaru, S Ohno, K Okamoto, T Suzuki, N Shirai, ... Medical Imaging 2017: Image Processing 10133, 488-494, 2017 | 34 | 2017 |
| Super-resolution imaging of mammograms based on the super-resolution convolutional neural network K Umehara, J Ota, T Ishida Open Journal of Medical Imaging 7 (4), 180-195, 2017 | 28 | 2017 |
| Deep learning–based post hoc CT denoising for myocardial delayed enhancement T Nishii, T Kobayashi, H Tanaka, A Kotoku, Y Ohta, Y Morita, K Umehara, ... Radiology 305 (1), 82-91, 2022 | 12 | 2022 |
| Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography J Ota, K Umehara, N Ishimaru, S Ohno, K Okamoto, T Suzuki, N Shirai, ... Medical Imaging 2017: Image Processing 10133, 509-517, 2017 | 10 | 2017 |
| Deep ensemble learning of virtual endoluminal views for polyp detection in CT colonography K Umehara, JJ Näppi, T Hironaka, D Regge, T Ishida, H Yoshida Medical Imaging 2017: Computer-Aided Diagnosis 10134, 108-113, 2017 | 8 | 2017 |
| Performance evaluation of super-resolution methods using deep-learning and sparse-coding for improving the image quality of magnified images in chest radiographs K Umehara, J Ota, N Ishimaru, S Ohno, K Okamoto, T Suzuki, T Ishida Open Journal of Medical Imaging 7 (3), 100-111, 2017 | 7 | 2017 |
| Generative adversarial network-based post-processed image super-resolution technology for accelerating brain MRI: comparison with compressed sensing W Ueki, T Nishii, K Umehara, J Ota, S Higuchi, Y Ohta, Y Nagai, ... Acta Radiologica 64 (1), 336-345, 2023 | 5 | 2023 |
| Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth T Kobayashi, T Nishii, K Umehara, J Ota, Y Ohta, T Fukuda, T Ishida Acta Radiologica 64 (5), 1831-1840, 2023 | 4 | 2023 |
| Super-resolution generative adversarial networks with static T2* WI-based subject-specific learning to improve spatial difference sensitivity in fMRI activation J Ota, K Umehara, J Kershaw, R Kishimoto, Y Hirano, Y Tachibana, ... Scientific Reports 12 (1), 10319, 2022 | 4 | 2022 |
| Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network T Misaka, N Asato, Y Ono, Y Ota, T Kobayashi, K Umehara, J Ota, ... Medicine 99 (47), e23138, 2020 | 4 | 2020 |
| Application of sparse-coding super-resolution to 16-bit DICOM images for improving the image resolution in MRI J Ota, K Umehara, N Ishimaru, T Ishida Open Journal of Medical Imaging 7 (4), 144-155, 2017 | 4 | 2017 |
| Deep learning-based post hoc CT denoising for the coronary perivascular fat attenuation index T Nishii, T Kobayashi, T Saito, A Kotoku, Y Ohta, S Kitahara, K Umehara, ... Academic Radiology 30 (11), 2505-2513, 2023 | 3 | 2023 |
| Deep super-learning of polyp images for computer-aided detection in CT colonography K Umehara, JJ Näppi, T Hironaka, T Ishida, D Regge, H Yoshida International Journal of Computer Assisted Radiology and Surgery 12, 278-279, 2017 | 3 | 2017 |
| A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic … Y Tachibana, M Nishimori, N Kitamura, K Umehara, J Ota, T Obata, ... arXiv preprint arXiv:2002.01891, 2020 | 2 | 2020 |
| A JROD survey: nationwide overview of radiotherapy data from 2015 to 2021 H Ohba, Y Nakada, H Numasaki, K Umehara, J Ota, Y Okuda, T Teshima, ... Journal of Radiation Research 64 (6), 904-910, 2023 | 1 | 2023 |
| 1. Deep Learning Super-resolution in Medical Imaging: What Is It and How to Use It K Umehara Nihon Hoshasen Gijutsu Gakkai Zasshi 76 (5), 524-533, 2020 | 1 | 2020 |
| Sparse coding super-resolution scheme for chest computed tomography J Ota, K Umehara, N Ishimaru, S Ohno, K Okamoto, T Suzuki, T Ishida Journal of Medical Imaging and Health Informatics 8 (5), 1043-1050, 2018 | 1 | 2018 |
| A Japanese registry study and systematic review of particle therapy for renal cell carcinoma H Ishikawa, T Arimura, K Maruo, H Kawamura, S Toyama, T Ogino, ... Journal of Radiation Research 64 (Supplement_1), i41-i48, 2023 | | 2023 |
| 1. 深層学習を用いた超解像技術と医用画像への応用 梅原健輔 日本放射線技術学会雑誌 76 (5), 524-533, 2020 | | 2020 |
Junko OtaNational Institutes for Quantum and Radiological Science and TechnologyVerified email at qst.go.jp
