| LINKS: Learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images L Wang, Y Gao, F Shi, G Li, JH Gilmore, W Lin, D Shen NeuroImage 108, 160-172, 2015 | 154 | 2015 |
| Segmentation of neonatal brain MR images using patch-driven level sets L Wang, F Shi, G Li, Y Gao, W Lin, JH Gilmore, D Shen NeuroImage 84, 141-158, 2014 | 146 | 2014 |
| Representation learning: a unified deep learning framework for automatic prostate MR segmentation S Liao, Y Gao, A Oto, D Shen International Conference on Medical Image Computing and Computer-Assisted …, 2013 | 137 | 2013 |
| Deformable MR prostate segmentation via deep feature learning and sparse patch matching Y Guo, Y Gao, D Shen IEEE transactions on medical imaging 35 (4), 1077-1089, 2015 | 126 | 2015 |
| Estimating CT image from MRI data using structured random forest and auto-context model T Huynh, Y Gao, J Kang, L Wang, P Zhang, J Lian, D Shen IEEE transactions on medical imaging 35 (1), 174-183, 2015 | 107 | 2015 |
| Fully convolutional networks for multi-modality isointense infant brain image segmentation D Nie, L Wang, Y Gao, D Shen 2016 IEEE 13Th international symposium on biomedical imaging (ISBI), 1342-1345, 2016 | 97 | 2016 |
| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 90 | 2018 |
| Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation L Wang, F Shi, Y Gao, G Li, JH Gilmore, W Lin, D Shen NeuroImage 89, 152-164, 2014 | 82 | 2014 |
| Estimating CT image from MRI data using 3D fully convolutional networks D Nie, X Cao, Y Gao, L Wang, D Shen Deep Learning and Data Labeling for Medical Applications, 170-178, 2016 | 78 | 2016 |
| Unsupervised deep feature learning for deformable registration of MR brain images G Wu, M Kim, Q Wang, Y Gao, S Liao, D Shen International Conference on Medical Image Computing and Computer-Assisted …, 2013 | 72 | 2013 |
| Prostate segmentation by sparse representation based classification Y Gao, S Liao, D Shen Medical physics 39 (10), 6372-6387, 2012 | 70 | 2012 |
| Sparse patch-based label propagation for accurate prostate localization in CT images S Liao, Y Gao, J Lian, D Shen IEEE transactions on medical imaging 32 (2), 419-434, 2012 | 63 | 2012 |
| Detecting anatomical landmarks for fast Alzheimer’s disease diagnosis J Zhang, Y Gao, Y Gao, BC Munsell, D Shen IEEE transactions on medical imaging 35 (12), 2524-2533, 2016 | 61 | 2016 |
| Learning to rank atlases for multiple-atlas segmentation G Sanroma, G Wu, Y Gao, D Shen IEEE transactions on medical imaging 33 (10), 1939-1953, 2014 | 56 | 2014 |
| Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest L Huang, Y Jin, Y Gao, KH Thung, D Shen, ... Neurobiology of aging 46, 180-191, 2016 | 51 | 2016 |
| Automated bone segmentation from dental CBCT images using patch‐based sparse representation and convex optimization L Wang, KC Chen, Y Gao, F Shi, S Liao, G Li, SGF Shen, J Yan, PKM Lee, ... Medical physics 41 (4), 043503, 2014 | 42 | 2014 |
| Alzheimer's disease diagnosis using landmark-based features from longitudinal structural MR images J Zhang, M Liu, L An, Y Gao, D Shen IEEE journal of biomedical and health informatics 21 (6), 1607-1616, 2017 | 40 | 2017 |
| Accurate segmentation of CT male pelvic organs via regression-based deformable models and multi-task random forests Y Gao, Y Shao, J Lian, AZ Wang, RC Chen, D Shen IEEE transactions on medical imaging 35 (6), 1532-1543, 2016 | 40 | 2016 |
| Hierarchical Lung Field Segmentation with Joint Shape and Appearance Sparse Learning Y Shao, Y Gao, Y Guo, Y Shi, X Yang, D Shen Medical Imaging, IEEE Transactions on, 1-1, 2014 | 40 | 2014 |
| Prostate segmentation in CT images via spatial-constrained transductive lasso Y Shi, S Liao, Y Gao, D Zhang, Y Gao, D Shen Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 29 | 2013 |
