| JCS: An explainable covid-19 diagnosis system by joint classification and segmentation YH Wu, SH Gao, J Mei, J Xu, DP Fan, RG Zhang, MM Cheng IEEE Transactions on Image Processing (TIP) 30, 3113-3126, 2021 | 515 | 2021 |
| Prediction of the development of pulmonary fibrosis using serial thin-section CT and clinical features in patients discharged after treatment for COVID-19 pneumonia M Yu, Y Liu, D Xu, R Zhang, L Lan, H Xu Korean journal of radiology 21 (6), 746, 2020 | 315 | 2020 |
| A preliminary examination of the diagnostic value of deep learning in hip osteoarthritis Y Xue, R Zhang, Y Deng, K Chen, T Jiang PloS one 12 (6), e0178992, 2017 | 183 | 2017 |
| Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas S Liang, R Zhang, D Liang, T Song, T Ai, C Xia, L Xia, Y Wang Genes 9 (8), 382, 2018 | 122 | 2018 |
| Survey on deep learning for pulmonary medical imaging J Ma, Y Song, X Tian, Y Hua, R Zhang, J Wu Frontiers of medicine 14 (4), 450-469, 2020 | 116 | 2020 |
| Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation M Wang, C Xia, L Huang, S Xu, C Qin, J Liu, Y Cao, P Yu, T Zhu, H Zhu, ... The Lancet Digital Health 2 (10), e506-e515, 2020 | 97 | 2020 |
| Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning W Liu, M Liu, X Guo, P Zhang, L Zhang, R Zhang, H Kang, Z Zhai, X Tao, ... European radiology 30, 3567-3575, 2020 | 95 | 2020 |
| Automatic detection and classification of rib fractures on thoracic CT using convolutional neural network: accuracy and feasibility QQ Zhou, J Wang, W Tang, ZC Hu, ZY Xia, XS Li, R Zhang, X Yin, ... Korean journal of radiology 21 (7), 869, 2020 | 94 | 2020 |
| Evaluating a fully automated pulmonary nodule detection approach and its impact on radiologist performance K Liu, Q Li, J Ma, Z Zhou, M Sun, Y Deng, W Tu, Y Wang, L Fan, C Xia, ... Radiology: Artificial Intelligence 1 (3), e180084, 2019 | 90 | 2019 |
| Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks Y Fang, W Li, X Chen, K Chen, H Kang, P Yu, R Zhang, J Liao, G Hong, ... European Radiology 31, 1831-1842, 2021 | 84 | 2021 |
| Deep network for the automatic segmentation and quantification of intracranial hemorrhage on CT J Xu, R Zhang, Z Zhou, C Wu, Q Gong, H Zhang, S Wu, G Wu, Y Deng, ... Frontiers in neuroscience 14, 541817, 2021 | 47 | 2021 |
| Automatic detection and classification of rib fractures based on patients’ CT images and clinical information via convolutional neural network QQ Zhou, W Tang, J Wang, ZC Hu, ZY Xia, R Zhang, X Fan, W Yong, ... European Radiology 31, 3815-3825, 2021 | 37 | 2021 |
| Using contrast-enhanced CT and non-contrast-enhanced CT to predict EGFR mutation status in NSCLC patients—a radiomics nomogram analysis X Yang, M Liu, Y Ren, H Chen, P Yu, S Wang, R Zhang, H Dai, C Wang European radiology, 1-11, 2022 | 31 | 2022 |
| Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework K Yang, J Liu, W Tang, H Zhang, R Zhang, J Gu, R Zhu, J Xiong, X Ru, ... European journal of radiology 129, 109013, 2020 | 29 | 2020 |
| Machine learning analysis for the noninvasive prediction of lymphovascular invasion in gastric cancer using PET/CT and enhanced CT-based radiomics and clinical variables L Fan, J Li, H Zhang, H Yin, R Zhang, J Zhang, X Chen Abdominal Radiology 47 (4), 1209-1222, 2022 | 28 | 2022 |
| Deep residual nets model for staging liver fibrosis on plain CT images Q Li, B Yu, X Tian, X Cui, R Zhang, Q Guo International Journal of Computer Assisted Radiology and Surgery 15, 1399-1406, 2020 | 23 | 2020 |
| RPLHR-CT dataset and transformer baseline for volumetric super-resolution from CT scans P Yu, H Zhang, H Kang, W Tang, CW Arnold, R Zhang International Conference on Medical Image Computing and Computer-Assisted …, 2022 | 19 | 2022 |
| Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system K Li, K Liu, Y Zhong, M Liang, P Qin, H Li, R Zhang, S Li, X Liu Quantitative imaging in medicine and surgery 11 (8), 3629, 2021 | 19 | 2021 |
| Development of a deep learning model for classifying thymoma as Masaoka-Koga stage I or II via preoperative CT images L Yang, W Cai, X Yang, H Zhu, Z Liu, X Wu, Y Lei, J Zou, B Zeng, X Tian, ... Annals of Translational Medicine 8 (6), 2020 | 19 | 2020 |
| Discrimination of smoking status by MRI based on deep learning method S Wang, R Zhang, Y Deng, K Chen, D Xiao, P Peng, T Jiang Quantitative Imaging in Medicine and Surgery 8 (11), 1113, 2018 | 18 | 2018 |
pengxin yuNortheastern UniversityVerified email at infervision.com
