| Saliency detection on light field N Li, J Ye, Y Ji, H Ling, J Yu Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 568 | 2014 |
| A weighted sparse coding framework for saliency detection N Li, B Sun, J Yu Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 157 | 2015 |
| Deep surface light fields A Chen, M Wu, Y Zhang, N Li, J Lu, S Gao, J Yu Proceedings of the ACM on Computer Graphics and Interactive Techniques 1 (1 …, 2018 | 75 | 2018 |
| Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists A Swiecicki, N Li, J O'Donnell, N Said, J Yang, RC Mather, WA Jiranek, ... Computers in biology and medicine 133, 104334, 2021 | 68 | 2021 |
| A data set and deep learning algorithm for the detection of masses and architectural distortions in digital breast tomosynthesis images M Buda, A Saha, R Walsh, S Ghate, N Li, A Święcicki, JY Lo, ... JAMA network open 4 (8), e2119100-e2119100, 2021 | 63 | 2021 |
| Personalized saliency and its prediction Y Xu, S Gao, J Wu, N Li, J Yu IEEE transactions on pattern analysis and machine intelligence 41 (12), 2975 …, 2018 | 44 | 2018 |
| Dynamic fluid surface reconstruction using deep neural network S Thapa, N Li, J Ye Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 36 | 2020 |
| Nimble: a non-rigid hand model with bones and muscles Y Li, L Zhang, Z Qiu, Y Jiang, N Li, Y Ma, Y Zhang, L Xu, J Yu ACM Transactions on Graphics (TOG) 41 (4), 1-16, 2022 | 33 | 2022 |
| Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model M Buda, A Saha, R Walsh, S Ghate, N Li, A Święcicki, JY Lo, ... arXiv preprint arXiv:2011.07995, 2020 | 32 | 2020 |
| Unsupervised non-rigid image distortion removal via grid deformation N Li, S Thapa, C Whyte, AW Reed, S Jayasuriya, J Ye Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 29 | 2021 |
| Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN. Y Xu, N Li, J Wu, J Yu, S Gao IJCAI, 3887-3893, 2017 | 24 | 2017 |
| Data from the breast cancer screening–digital breast tomosynthesis (bcs-dbt) M Buda, A Saha, R Walsh, S Ghate, N Li, A Święcicki, JY Lo, J Yang, ... Data from The Cancer Imaging Archive, 2020 | 10 | 2020 |
| Learning to remove refractive distortions from underwater images S Thapa, N Li, J Ye Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 8 | 2021 |
| Rotational crossed-slit light field N Li, H Lin, B Sun, M Zhou, J Yu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 7 | 2016 |
| Corner-surround contrast for saliency detection Q Zhou, N Li, Y Yang, P Chen, W Liu Proceedings of the 21st International Conference on Pattern Recognition …, 2012 | 6 | 2012 |
| Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images A Swiecicki, M Buda, A Saha, N Li, SV Ghate, R Walsh, MA Mazurowski Medical Imaging 2020: Computer-Aided Diagnosis 11314, 514-519, 2020 | 5 | 2020 |
| Breast Cancer Screening–Digital Breast Tomosynthesis (BCS-DBT)[Data set] M Buda, A Saha, R Walsh, S Ghate, N Li, A Swiecicki, JY Lo, J Yang, ... Cancer Imaging Arch, 2020 | 5 | 2020 |
| Free-view face relighting using a hybrid parametric neural model on a small-olat dataset Y Wang, K He, T Zhou, K Yao, N Li, L Xu, J Yu International Journal of Computer Vision 131 (4), 1002-1021, 2023 | 3 | 2023 |
| Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms A Swiecicki, N Said, J O'Donnell, M Buda, N Li, WA Jiranek, ... Medical Imaging 2020: Computer-Aided Diagnosis 11314, 863-868, 2020 | 3 | 2020 |
| Efficient 3D face recognition in uncontrolled environment Y Ding, N Li, SS Young, J Ye Advances in Visual Computing: 14th International Symposium on Visual …, 2019 | 3 | 2019 |
Jinwei YeGeorge Mason UniversityVerified email at gmu.edu
