| Convolutional neural networks: an overview and application in radiology R Yamashita, M Nishio, RKG Do, K Togashi Insights into imaging 9, 611-629, 2018 | 5235 | 2018 |
| Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods M Nishio, S Noguchi, H Matsuo, T Murakami Scientific reports 10 (1), 17532, 2020 | 182 | 2020 |
| Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional … M Nishio, O Sugiyama, M Yakami, S Ueno, T Kubo, T Kuroda, K Togashi PloS one 13 (7), e0200721, 2018 | 173 | 2018 |
| Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization M Nishio, M Nishizawa, O Sugiyama, R Kojima, M Yakami, T Kuroda, ... PloS one 13 (4), e0195875, 2018 | 123 | 2018 |
| Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques S Noguchi, M Nishio, M Yakami, K Nakagomi, K Togashi Computers in biology and medicine 121, 103767, 2020 | 121 | 2020 |
| N stage disease in patients with non–small cell lung cancer: efficacy of quantitative and qualitative assessment with STIR turbo spin-echo imaging, diffusion-weighted MR … Y Ohno, H Koyama, T Yoshikawa, M Nishio, N Aoyama, Y Onishi, ... Radiology 261 (2), 605-615, 2011 | 120 | 2011 |
| Convolutional auto-encoder for image denoising of ultra-low-dose CT M Nishio, C Nagashima, S Hirabayashi, A Ohnishi, K Sasaki, T Sagawa, ... Heliyon 3 (8), 2017 | 110 | 2017 |
| Magnetic resonance imaging for lung cancer H Koyama, Y Ohno, S Seki, M Nishio, T Yoshikawa, S Matsumoto, ... Journal of thoracic imaging 28 (3), 138-150, 2013 | 102 | 2013 |
| Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018) R Yamashita, M Nishio, RKG Do, K Togashi | 96 | 2018 |
| Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT S Koyasu, M Nishio, H Isoda, Y Nakamoto, K Togashi Annals of Nuclear Medicine 34, 49-57, 2020 | 90 | 2020 |
| Solitary pulmonary nodules: Comparison of dynamic first-pass contrast-enhanced perfusion area-detector CT, dynamic first-pass contrast-enhanced MR imaging, and FDG PET/CT Y Ohno, M Nishio, H Koyama, S Seki, M Tsubakimoto, Y Fujisawa, ... Radiology 274 (2), 563-575, 2015 | 89 | 2015 |
| Dynamic contrast-enhanced CT and MRI for pulmonary nodule assessment Y Ohno, M Nishio, H Koyama, S Miura, T Yoshikawa, S Matsumoto, ... American Journal of Roentgenology 202 (3), 515-529, 2014 | 82 | 2014 |
| Homology-based image processing for automatic classification of histopathological images of lung tissue M Nishio, M Nishio, N Jimbo, K Nakane Cancers 13 (6), 1192, 2021 | 80 | 2021 |
| Value of diffusion-weighted MR imaging using various parameters for assessment and characterization of solitary pulmonary nodules H Koyama, Y Ohno, S Seki, M Nishio, T Yoshikawa, S Matsumoto, ... European journal of radiology 84 (3), 509-515, 2015 | 77 | 2015 |
| Automatic segmentation of the uterus on MRI using a convolutional neural network Y Kurata, M Nishio, A Kido, K Fujimoto, M Yakami, H Isoda, K Togashi Computers in biology and medicine 114, 103438, 2019 | 72 | 2019 |
| Improving breast mass classification by shared data with domain transformation using a generative adversarial network C Muramatsu, M Nishio, T Goto, M Oiwa, T Morita, M Yakami, T Kubo, ... Computers in biology and medicine 119, 103698, 2020 | 69 | 2020 |
| Diffusion-weighted MR imaging vs. multi-detector row CT: Direct comparison of capability for assessment of management needs for anterior mediastinal solitary tumors S Seki, H Koyama, Y Ohno, M Nishio, D Takenaka, Y Maniwa, T Itoh, ... European journal of radiology 83 (5), 835-842, 2014 | 64 | 2014 |
| Emphysema quantification by low-dose CT: potential impact of adaptive iterative dose reduction using 3D processing M Nishio, S Matsumoto, Y Ohno, N Sugihara, H Inokawa, T Yoshikawa, ... American Journal of Roentgenology 199 (3), 595-601, 2012 | 55 | 2012 |
| Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI H Matsuo, M Nishio, T Kanda, Y Kojita, AK Kono, M Hori, M Teshima, ... Scientific Reports 10 (1), 19388, 2020 | 50 | 2020 |
| Computer-aided diagnosis for lung cancer: usefulness of nodule heterogeneity M Nishio, C Nagashima Academic radiology 24 (3), 328-336, 2017 | 49 | 2017 |
