| Differentiating Benign from Malignant Renal Tumors Using T2‐and Diffusion‐Weighted Images: A Comparison of Deep Learning and Radiomics Models Versus Assessment from Radiologists Q Xu, QQ Zhu, H Liu, LF Chang, SF Duan, WQ Dou, SY Li, J Ye Journal of Magnetic Resonance Imaging 55 (4), 1251-1259, 2022 | 33 | 2022 |
| An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions L Wang, L Chang, R Luo, X Cui, H Liu, H Wu, Y Chen, Y Zhang, C Wu, ... European Radiology, 1-11, 2022 | 18 | 2022 |
| A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images H Zhao, C Liu, J Ye, L Chang, Q Xu, B Shi, L Liu, Y Yin, B Shi Endokrynologia Polska 72 (3), 217-225, 2021 | 17 | 2021 |
| DARWIN: A Highly Flexible Platform for Imaging Research in Radiology L Chang, W Zhuang, R Wu, S Feng, H Liu, J Yu, J Ding, Z Wang, J Zhang arXiv preprint arXiv:2009.00908, 2020 | 5 | 2020 |
| A Deep Learning System to Predict the Histopathological Results From Urine Cytopathological Images Y Liu, S Jin, Q Shen, L Chang, S Fang, Y Fan, H Peng, W Yu Frontiers in Oncology 12, 901586, 2022 | 4 | 2022 |
| Prediction of microvascular invasion in hepatocellular carcinoma based on preoperative Gd-EOB-DTPA-enhanced MRI: Comparison of predictive performance among 2D, 2D-expansion and … T Wang, Z Li, H Yu, C Duan, W Feng, L Chang, J Yu, F Liu, J Gao, Y Zang, ... Frontiers in Oncology 13, 987781, 2023 | 3 | 2023 |
| Unleashing the Power of Language Models in Clinical Settings: A Trailblazing Evaluation Unveiling Novel Test Design Q Li, X Min medRxiv, 2023.07. 11.23292512, 2023 | | 2023 |
| Classification in cryo-electron tomograms I Gubins, G van der Schot, RC Veltkamp, FG Förster, X Du, X Zeng, Z Zhu, ... SHREC’19 Track, 2019 | | 2019 |
Xiangrui (Taylor) ZengPostdoctoral research fellow, Harvard Medical SchoolVerified email at mgh.harvard.edu
