| Temporal action localization in untrimmed videos via multi-stage cnns Z Shou, D Wang, SF Chang Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 1076 | 2016 |
| Dividing and aggregating network for multi-view action recognition D Wang, W Ouyang, W Li, D Xu Proceedings of the European conference on computer vision (ECCV), 451-467, 2018 | 85 | 2018 |
| Cross-media event extraction and recommendation D Lu, C Voss, F Tao, X Ren, R Guan, R Korolov, T Zhang, D Wang, H Li, ... Proceedings of the 2016 conference of the North American chapter of the …, 2016 | 18 | 2016 |
| Masked multi-task network for case-level intracranial hemorrhage classification in brain ct volumes D Wang, C Wang, L Masters, M Barnett International Conference on Medical Image Computing and Computer-Assisted …, 2020 | 9 | 2020 |
| A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis M Barnett, D Wang, H Beadnall, A Bischof, D Brunacci, H Butzkueven, ... npj Digital Medicine 6 (1), 196, 2023 | 5 | 2023 |
| Learning from pseudo-labels: deep networks improve consistency in longitudinal brain volume estimation G Zhan, D Wang, M Cabezas, L Bai, K Kyle, W Ouyang, M Barnett, ... Frontiers in Neuroscience 17, 1196087, 2023 | 5 | 2023 |
| Multiple sclerosis lesion segmentation: revisiting weighting mechanisms for federated learning D Liu, M Cabezas, D Wang, Z Tang, L Bai, G Zhan, Y Luo, K Kyle, L Ly, ... Frontiers in Neuroscience 17, 1167612, 2023 | 5 | 2023 |
| Real world validation of an AI-based CT hemorrhage detection tool D Wang, R Jin, CC Shieh, AY Ng, H Pham, T Dugal, M Barnett, L Winoto, ... Frontiers in Neurology 14, 1177723, 2023 | 2 | 2023 |
| Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training L Bai, D Wang, M Barnett, M Cabezas, W Cai, F Calamante, K Kyle, D Liu, ... arXiv preprint arXiv:2308.16376, 2023 | 1 | 2023 |
| Federated learning aggregation via a reinforcement learning policy B Häon, A Dunn, J Kim, C Wang, D Wang 2023 ICLR First Workshop on {\textquotedblleft} Machine Learning {\&} Global …, 2023 | 1 | 2023 |
| AI-BASED MRI MONITORING IN MULTIPLE SCLEROSIS: REAL-WORLD CLINICAL VALIDATION M Barnett, D Wang, H Beadnall, A Bischof, D Brunacci, H Butzkueven, ... medRxiv, 2023.08. 14.23293959, 2023 | 1 | 2023 |
| Action Recognition in Multi-view Videos D Wang | 1 | 2018 |
| How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks D Wang, P Liu, H Wang, H Beadnall, K Kyle, L Ly, M Cabezas, G Zhan, ... arXiv preprint arXiv:2404.03451, 2024 | | 2024 |
| The MSBase Imaging Repository-Delivering Real-World Imaging Data to Multiple Sclerosis Researchers with Seamless Clinical Integration and Intelligent Data Management CC Shieh, C Wang, H Beadnall, D Wang, R More, H Butzkueven, ... MULTIPLE SCLEROSIS JOURNAL 29, 584-585, 2023 | | 2023 |
| AI Adoption in Real-World Clinical Neuroimaging Applications: Practical Challenges and Solutions D Wang | | 2023 |
| Learning from pseudo-labels: deep networks improve classical longitudinal brain volume change estimation for patients with MS G Zhan, D Wang, M Cabezas, M Barnett, C Wang MULTIPLE SCLEROSIS JOURNAL 28 (3_ SUPPL), 471-472, 2022 | | 2022 |
| EventNet Version 1.1 Technical Report D Wang, Z Shou, H Liu, SF Chang arXiv preprint arXiv:1605.07289, 2016 | | 2016 |
Shih-Fu ChangProfessor of Electrical Engineering and Computer Science, Columbia UniversityVerified email at columbia.edu
