Serena Yeung
About
In The Last Decade
Serena Yeung
43 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Computer Vision and Pattern Recognition 1.4k
- Artificial Intelligence 1.0k
- Radiology, Nuclear Medicine and Imaging 542
- Biomedical Engineering 501
- Surgery 272
Countries citing papers authored by Serena Yeung
This map shows the geographic impact of Serena Yeung's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Serena Yeung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Serena Yeung more than expected).
Fields of papers citing papers by Serena Yeung
This network shows the impact of papers produced by Serena Yeung. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Serena Yeung. The network helps show where Serena Yeung may publish in the future.
Co-authorship network of co-authors of Serena Yeung
This figure shows the co-authorship network connecting the top 25 collaborators of Serena Yeung. A scholar is included among the top collaborators of Serena Yeung based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Serena Yeung. Serena Yeung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Title | Journal | Authors | Indexed citations |
|---|---|---|---|---|
| 1 | Artificial intelligence–powered 3D analysis of video-based caregiver-child interactions | Science Advances | Christopher J. Howard, Maria Xenochristou et al. | 1 |
| 2 | Cryogenic electron tomography and elemental analysis of mitochondrial granules in human retinal ganglion cells | Structure | Gong‐Her Wu, Hiren Patel et al. | 0 |
| 3 | Using artificial intelligence to model expert panel diagnosis of cholecystitis severity | Surgical Endoscopy | Griffin Olsen, Emmett D. Goodman et al. | 0 |
| 4 | Hospitalization prediction from the emergency department using computer vision AI with short patient video clips | npj Digital Medicine | Maria Xenochristou, Malathi Srinivasan et al. | 2 |
| 5 | Multimodal Foundation Models for Medical Imaging - A Systematic Review and Implementation Guidelines | medRxiv | Shih-Cheng Huang, Malte Jensen et al. | 5 |
| 6 | Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles | Nature Communications | Jeffrey Nirschl, Alejandro Lozano et al. | 9 |
| 7 | Hyperbolic Deep Learning in Computer Vision: A Survey | International Journal of Computer Vision | Pascal Mettes, Martin Keller‐Ressel et al. | 9 |
| 8 | Self-supervised learning for medical image classification: a systematic review and implementation guidelines breakdown → | npj Digital Medicine | Shih-Cheng Huang, Anuj Pareek et al. | 174 |
| 9 | Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models | Yuhui Zhang, Michihiro Yasunaga et al. | 1 | |
| 10 | Comparing spatial patterns of marine vessels between vessel-tracking data and satellite imagery | Frontiers in Marine Science | Shinnosuke Nakayama, Elizabeth R. Selig et al. | 1 |
| 11 | Developing medical imaging AI for emerging infectious diseases | Nature Communications | Shih-Cheng Huang, Akshay Chaudhari et al. | 18 |
| 12 | Using AI and computer vision to analyze technical proficiency in robotic surgery | Surgical Endoscopy | Emmett D. Goodman, Aaron J. Dawes et al. | 20 |
| 13 | Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy | Scientific Reports | F. Christopher Holsinger, Julia E. Noel et al. | 17 |
| 14 | Parents’ Perspectives on Using Artificial Intelligence to Reduce Technology Interference During Early Childhood: Cross-sectional Online Survey | Journal of Medical Internet Research | Jill R. Glassman, Kathryn L. Humphreys et al. | 9 |
| 15 | A computer vision system for deep learning-based detection of patient mobilization activities in the ICU | npj Digital Medicine | Serena Yeung, Jeffrey K. Jopling et al. | 78 |
| 16 | 3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities. | Bingbin Liu, Michelle Guo et al. | 5 | |
| 17 | Vision-Based Prediction of ICU Mobility Care Activities using Recurrent Neural Networks | Infoscience (Ecole Polytechnique Fédérale de Lausanne) | Serena Yeung, Jeffrey K. Jopling et al. | 3 |
| 18 | Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance | Infoscience (Ecole Polytechnique Fédérale de Lausanne) | Albert Haque, Michelle Guo et al. | 7 |
| 19 | Vision-Based Hand Hygiene Monitoring in Hospitals | Infoscience (Ecole Polytechnique Fédérale de Lausanne) | Serena Yeung, Alexandre Alahi et al. | 6 |
| 20 | Viewpoint Invariant 3D Human Pose Estimation with Recurrent Error Feedback | arXiv (Cornell University) | Albert Haque, Boya Peng et al. | 5 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.