| Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer Y Huang, C Liang, L He, J Tian, C Liang, X Chen, Z Ma, Z Liu Journal of clinical oncology 34 (18), 2157-2164, 2016 | 1546 | 2016 |
| Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non—small cell lung cancer Y Huang, Z Liu, L He, X Chen, D Pan, Z Ma, C Liang, J Tian, C Liang Radiology 281 (3), 947-957, 2016 | 682 | 2016 |
| Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation S Wang, M Zhou, Z Liu, Z Liu, D Gu, Y Zang, D Dong, O Gevaert, J Tian Medical image analysis 40, 172-183, 2017 | 469 | 2017 |
| Radiomics of multiparametric MRI for pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study Z Liu, Z Li, J Qu, R Zhang, X Zhou, L Li, K Sun, Z Tang, H Jiang, H Li, ... Clinical Cancer Research 25 (12), 3538-3547, 2019 | 357 | 2019 |
| Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI C Liu, C Liang, Z Liu, S Zhang, B Huang European journal of radiology 82 (12), e782-e789, 2013 | 270 | 2013 |
| Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule L He, Y Huang, Z Ma, C Liang, C Liang, Z Liu Scientific reports 6 (1), 34921, 2016 | 260 | 2016 |
| Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study D Dong, MJ Fang, L Tang, XH Shan, JB Gao, F Giganti, RP Wang, X Chen, ... Annals of oncology 31 (7), 912-920, 2020 | 246 | 2020 |
| CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma Y Zhou, L He, Y Huang, S Chen, P Wu, W Ye, Z Liu, C Liang Abdominal radiology 42, 1695-1704, 2017 | 210 | 2017 |
| The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer C Liang, Y Huang, L He, X Chen, Z Ma, D Dong, J Tian, C Liang, Z Liu Oncotarget 7 (21), 31401, 2016 | 175 | 2016 |
| A new approach to predict progression-free survival in stage IV EGFR-mutant NSCLC patients with EGFR-TKI therapy J Song, J Shi, D Dong, M Fang, W Zhong, K Wang, N Wu, Y Huang, Z Liu, ... Clinical Cancer Research 24 (15), 3583-3592, 2018 | 164 | 2018 |
| X-net: Brain stroke lesion segmentation based on depthwise separable convolution and long-range dependencies K Qi, H Yang, C Li, Z Liu, M Wang, Q Liu, S Wang Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 144 | 2019 |
| A radiomics approach with CNN for shear-wave elastography breast tumor classification Y Zhou, J Xu, Q Liu, C Li, Z Liu, M Wang, H Zheng, S Wang IEEE Transactions on Biomedical Engineering 65 (9), 1935-1942, 2018 | 142 | 2018 |
| AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms H Sun, C Li, B Liu, Z Liu, M Wang, H Zheng, DD Feng, S Wang Physics in Medicine & Biology 65 (5), 055005, 2020 | 134 | 2020 |
| Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast‐enhanced MR images W Zhou, L Zhang, K Wang, S Chen, G Wang, Z Liu, C Liang Journal of Magnetic Resonance Imaging 45 (5), 1476-1484, 2017 | 128 | 2017 |
| Predicting COVID-19 malignant progression with AI techniques X Bai, C Fang, Y Zhou, S Bai, Z Liu, Q Chen, Y Xu, T Xia, S Gong, X Xie, ... MedRxiv, 2020.03. 20.20037325, 2020 | 121 | 2020 |
| Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma G Wang, L He, C Yuan, Y Huang, Z Liu, C Liang European journal of radiology 98, 100-106, 2018 | 120 | 2018 |
| Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images L Yan, Z Liu, G Wang, Y Huang, Y Liu, Y Yu, C Liang Academic Radiology 22 (9), 1115-1121, 2015 | 117 | 2015 |
| Annotation-efficient deep learning for automatic medical image segmentation S Wang, C Li, R Wang, Z Liu, M Wang, H Tan, Y Wu, X Liu, H Sun, R Yang, ... Nature communications 12 (1), 5915, 2021 | 108 | 2021 |
| CT-based radiomics signature for differentiating Borrmann type IV gastric cancer from primary gastric lymphoma Z Ma, M Fang, Y Huang, L He, X Chen, C Liang, X Huang, Z Cheng, ... European Journal of Radiology 91, 142-147, 2017 | 106 | 2017 |
| Liver diffusion-weighted MR imaging: reproducibility comparison of ADC measurements obtained with multiple breath-hold, free-breathing, respiratory-triggered, and navigator … X Chen, L Qin, D Pan, Y Huang, L Yan, G Wang, Y Liu, C Liang, Z Liu Radiology 271 (1), 113-125, 2014 | 100 | 2014 |
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