
Serena Yeung
Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering
Department of Biomedical Data Science
Bio
Dr. Serena Yeung is an Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering at Stanford University. Her research focus is on developing artificial intelligence and machine learning algorithms to enable new capabilities in biomedicine and healthcare. She has extensive expertise in deep learning and computer vision, and has developed computer vision algorithms for analyzing diverse types of visual data ranging from video capture of human behavior, to medical images and cell microscopy images.
Dr. Yeung leads the Medical AI and Computer Vision Lab at Stanford. She is affiliated with the Stanford Artificial Intelligence Laboratory, the Clinical Excellence Research Center, the Center for Artificial Intelligence in Medicine & Imaging, the Center for Human-Centered Artificial Intelligence, and Bio-X. She also serves on the NIH Advisory Committee to the Director Working Group on Artificial Intelligence.
Academic Appointments
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Assistant Professor, Department of Biomedical Data Science
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Assistant Professor (By courtesy), Computer Science
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Assistant Professor (By courtesy), Electrical Engineering
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Member, Bio-X
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Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
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Member, Wu Tsai Neurosciences Institute
Honors & Awards
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Harvard Technology for Equitable and Accessible Medicine Fellowship, Harvard University (2018 - 2019)
Professional Education
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Postdoctoral Fellow, Harvard University (2019)
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Ph.D., Stanford University (2018)
2023-24 Courses
- Configuration of the US Healthcare System and the Application of Big Data/Analytics
BIODS 210 (Spr) -
Independent Studies (16)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr) - Advanced Reading and Research
CS 499P (Aut, Win, Spr) - Curricular Practical Training
CS 390A (Win, Spr) - Curricular Practical Training
CS 390B (Win) - Curricular Practical Training
CS 390C (Win) - Directed Reading and Research
BIODS 299 (Aut, Win, Sum) - Directed Reading and Research
BIOMEDIN 299 (Aut) - Graduate Research on Biomedical Data Science
BIODS 399 (Aut, Win, Spr, Sum) - Independent Project
CS 399 (Aut, Win, Spr) - Independent Work
CS 199 (Aut, Win, Spr) - Part-time Curricular Practical Training
CS 390D (Win) - Ph.D. Research
CME 400 (Aut, Win, Spr) - Programming Service Project
CS 192 (Win) - Senior Project
CS 191 (Aut, Win) - Supervised Undergraduate Research
CS 195 (Win, Spr) - Writing Intensive Senior Research Project
CS 191W (Aut, Win, Spr)
- Advanced Reading and Research
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Prior Year Courses
2022-23 Courses
- Artificial Intelligence in Healthcare
BIODS 220, BIOMEDIN 220, CS 271 (Aut) - Configuration of the US Healthcare System and the Application of Big Data/Analytics
BIODS 210 (Spr) - Facial Plastic and Reconstructive Surgery
OTOHNS 209 (Spr, Sum)
2021-22 Courses
- Artificial Intelligence in Healthcare
BIODS 220, BIOMEDIN 220, CS 271 (Aut) - Configuration of the US Healthcare System and the Application of Big Data/Analytics
BIODS 210 (Spr) - Facial Plastic and Reconstructive Surgery
OTOHNS 209 (Spr, Sum)
2020-21 Courses
- Artificial Intelligence in Healthcare
BIODS 220, BIOMEDIN 220, CS 271 (Aut) - Stakeholder Competencies for Artificial Intelligence in Healthcare
BIODS 388, BIOMEDIN 388 (Aut) - Workshop in Biostatistics
BIODS 260A, STATS 260A (Aut)
- Artificial Intelligence in Healthcare
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Rachael Kretsch, Stefania Moroianu -
Postdoctoral Faculty Sponsor
Anita Rau, Xiaohan Wang, Zeyu Wang -
Doctoral Dissertation Advisor (AC)
Josiah Aklilu, Laura Bravo Sánchez, James Burgess, Jeffrey Gu, Mars Huang, Ali Mottaghi, Jen Weng, Orr Zohar -
Master's Program Advisor
Maya Czeneszew, Isaac Gorelik, Jennifer Xu -
Doctoral (Program)
Sanket Gupte, Elana Simon, Yuhui Zhang
All Publications
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Ethical and Legal Aspects of Ambient Intelligence in Hospitals.
JAMA
2020
View details for DOI 10.1001/jama.2019.21699
View details for PubMedID 31977033
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A computer vision system for deep learning-based detection of patient mobilization activities in the ICU.
NPJ digital medicine
2019; 2: 11
Abstract
Early and frequent patient mobilization substantially mitigates risk for post-intensive care syndrome and long-term functional impairment. We developed and tested computer vision algorithms to detect patient mobilization activities occurring in an adult ICU. Mobility activities were defined as moving the patient into and out of bed, and moving the patient into and out of a chair. A data set of privacy-safe-depth-video images was collected in the Intermountain LDS Hospital ICU, comprising 563 instances of mobility activities and 98,801 total frames of video data from seven wall-mounted depth sensors. In all, 67% of the mobility activity instances were used to train algorithms to detect mobility activity occurrence and duration, and the number of healthcare personnel involved in each activity. The remaining 33% of the mobility instances were used for algorithm evaluation. The algorithm for detecting mobility activities attained a mean specificity of 89.2% and sensitivity of 87.2% over the four activities; the algorithm for quantifying the number of personnel involved attained a mean accuracy of 68.8%.
View details for DOI 10.1038/s41746-019-0087-z
View details for PubMedID 31304360
View details for PubMedCentralID PMC6550251
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Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
INTERNATIONAL JOURNAL OF COMPUTER VISION
2018; 126 (2-4): 375–89
View details for DOI 10.1007/s11263-017-1013-y
View details for Web of Science ID 000425619100013
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Bedside Computer Vision - Moving Artificial Intelligence from Driver Assistance to Patient Safety.
The New England journal of medicine
2018; 378 (14): 1271–73
View details for PubMedID 29617592
- 3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities Machine Learning in Healthcare 2018
- Computer Vision-based Descriptive Analytics of Seniors’ Daily Activities for Long-term Health Monitoring Machine Learning in Healthcare 2018
- Dynamic Task Prioritization for Multitask Learning European Conference on Computer Vision 2018
- Neural Graph Matching Networks for Fewshot 3D Action Recognition European Conference on Computer Vision 2018
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Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
IEEE. 2018: 691–99
View details for DOI 10.1109/WACV.2018.00081
View details for Web of Science ID 000434349200075
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Scaling Human-Object Interaction Recognition through Zero-Shot Learning
IEEE. 2018: 1568–76
View details for DOI 10.1109/WACV.2018.00181
View details for Web of Science ID 000434349200169
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Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos
European Conference on Computer Vision
2018: 569–86
View details for DOI 10.1007/978-3-030-01219-9_34
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Learning to Learn from Noisy Web Videos
IEEE. 2017: 7455–63
View details for DOI 10.1109/CVPR.2017.788
View details for Web of Science ID 000418371407059
- Towards Vision-Based Smart Hospitals: A System for Tracking and Monitoring Hand Hygiene Compliance Machine Learning in Healthcare 2017
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Jointly Learning Energy Expenditures and Activities using Egocentric Multimodal Signals
IEEE. 2017: 6817–26
View details for DOI 10.1109/CVPR.2017.721
View details for Web of Science ID 000418371406096
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End-to-end Learning of Action Detection from Frame Glimpses in Video
Computer Vision and Pattern Recognition
2016: 2678–87
View details for DOI 10.1109/cvpr.2016.293
- Towards Viewpoint Invariant 3D Human Pose Estimation European Conference on Computer Vision 2016
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Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
IEEE. 2011
View details for Web of Science ID 000295615803081