| Predicting rectal cancer response to neoadjuvant chemoradiotherapy using deep learning of diffusion kurtosis MRI XY Zhang, L Wang, HT Zhu, ZW Li, M Ye, XT Li, YJ Shi, HC Zhu, YS Sun Radiology 296 (1), 56-64, 2020 | 78 | 2020 |
| Prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer using a deep learning (DL) method YH Qu, HT Zhu, K Cao, XT Li, M Ye, YS Sun Thoracic Cancer 11 (3), 651-658, 2020 | 74 | 2020 |
| Deep learning assisted differentiation of hepatocellular carcinoma from focal liver lesions: choice of four-phase and three-phase CT imaging protocol W Shi, S Kuang, S Cao, B Hu, S Xie, S Chen, Y Chen, D Gao, Y Chen, ... Abdominal Radiology 45, 2688-2697, 2020 | 50 | 2020 |
| Deeptag: An unsupervised deep learning method for motion tracking on cardiac tagging magnetic resonance images M Ye, M Kanski, D Yang, Q Chang, Z Yan, Q Huang, L Axel, D Metaxas Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 40 | 2021 |
| Multiphase convolutional dense network for the classification of focal liver lesions on dynamic contrast-enhanced computed tomography SE Cao, LQ Zhang, SC Kuang, WQ Shi, B Hu, SD Xie, YN Chen, H Liu, ... World journal of gastroenterology 26 (25), 3660, 2020 | 36 | 2020 |
| Deep learning‐assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases HB Zhu, D Xu, M Ye, L Sun, XY Zhang, XT Li, P Nie, BC Xing, YS Sun International Journal of Cancer 148 (7), 1717-1730, 2021 | 28 | 2021 |
| PC-U net: Learning to jointly reconstruct and segment the cardiac walls in 3D from CT data M Ye, Q Huang, D Yang, P Wu, J Yi, L Axel, D Metaxas Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC …, 2021 | 19 | 2021 |
| Deeprecon: Joint 2d cardiac segmentation and 3d volume reconstruction via a structure-specific generative method Q Chang, Z Yan, M Zhou, D Liu, K Sawalha, M Ye, Q Zhangli, M Kanski, ... International Conference on Medical Image Computing and Computer-Assisted …, 2022 | 17 | 2022 |
| Deformer: Integrating transformers with deformable models for 3d shape abstraction from a single image D Liu, X Yu, M Ye, Q Zhangli, Z Li, Z Zhang, DN Metaxas Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 6 | 2023 |
| SequenceMorph: A unified unsupervised learning framework for motion tracking on cardiac image sequences M Ye, D Yang, Q Huang, M Kanski, L Axel, DN Metaxas IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (8), 10409 …, 2023 | 5 | 2023 |
| Cardiac MR image sequence segmentation with temporal motion encoding P Wu, Q Huang, J Yi, H Qu, M Ye, L Axel, D Metaxas Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020 …, 2020 | 5 | 2020 |
| Fill the k-space and refine the image: Prompting for dynamic and multi-contrast MRI reconstruction B Xin, M Ye, L Axel, DN Metaxas International Workshop on Statistical Atlases and Computational Models of …, 2023 | 4 | 2023 |
| An unsupervised 3D recurrent neural network for slice misalignment correction in cardiac MR imaging Q Chang, Z Yan, M Ye, K Mikael, S Al’Aref, L Axel, DN Metaxas International Workshop on Statistical Atlases and Computational Models of …, 2021 | 4 | 2021 |
| Unsupervised Exemplar-Based Image-to-Image Translation and Cascaded Vision Transformers for Tagged and Untagged Cardiac Cine MRI Registration M Ye, M Kanski, D Yang, L Axel, D Metaxas Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 2 | 2024 |
| Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging M Ye, D Yang, M Kanski, L Axel, D Metaxas Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 2 | 2023 |
| Supplementary Material for Rethinking Deep Unrolled Model for Accelerated MRI Reconstruction B Xin, M Ye, L Axel, DN Metaxas | | |
| Supplementary Material for DeFormer: Integrating Transformers with Deformable Models for 3D Shape Abstraction from a Single Image XY Di Liu, M Ye, Q Zhangli, Z Li, Z Zhang, DN Metaxas | | |
Dimitris N. MetaxasBoard of Governors Professor of Computer Science, Rutgers UniversityVerified email at cs.rutgers.edu
