Adam Wang
Stanford University · Rheumatology
Active 1989–2024
About
Adam Wang is an Assistant Professor of Radiology and, by courtesy, of Electrical Engineering at Stanford University. He is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI). His role involves advancing research in artificial intelligence applications within medicine and imaging, contributing to the development of innovative solutions in healthcare through AI. His work is focused on integrating AI techniques into medical imaging to improve diagnosis, treatment, and patient outcomes, leveraging his expertise in both radiology and electrical engineering.
Research signals
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Research topics
- Computer Science
- Artificial Intelligence
- Machine Learning
- Medicine
- Data Mining
- Data science
- Radiology
- Geography
- Computer vision
- Acoustics
- Materials science
- Mechanics
- Physics
- Mathematics
Selected publications
On Interpretability of Artificial Neural Networks: A Survey
IEEE Transactions on Radiation and Plasma Medical Sciences · 2021 · 494 citations
Senior authorCorresponding- Artificial Intelligence
- Artificial Intelligence
- Computer Science
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become one of the primary obstacles for their wide adoption in mission-critical applications such as medical diagnosis and therapy. Because of the huge potentials of deep learning, increasing the interpretability of deep neural networks has recently attracted much research attention. In this paper, we propose a simple but comprehensive taxonomy for interpretability, systematically review recent studies in improving interpretability of neural networks, describe applications of interpretability in medicine, and discuss possible future research directions of interpretability, such as in relation to fuzzy logic and brain science.
Deep learning for tomographic image reconstruction
Nature Machine Intelligence · 2020 · 526 citations
1st authorCorresponding- Artificial Intelligence
- Computer Science
- Artificial Intelligence
Shock-induced bubble collapse near solid materials: effect of acoustic impedance
Journal of Fluid Mechanics · 2020 · 38 citations
- Mechanics
- Materials science
- Acoustics
Abstract
Frequent coauthors
- 267 shared
Hengyong Yu
University of Massachusetts Lowell
- 152 shared
Wenxiang Cong
Rensselaer Polytechnic Institute
- 144 shared
Hongming Shan
- 137 shared
Wenxiang Cong
- 123 shared
Chuang Niu
Rensselaer Polytechnic Institute
- 79 shared
Michael W. Vannier
Lagrange Laboratory
- 77 shared
Mannudeep K. Kalra
Harvard University
- 75 shared
Pingkun Yan
Rensselaer Polytechnic Institute
Education
- 1992
PhD, Electrical & Computer Engineering
University at Buffalo
- 1991
MS, Electrical & Computer Engineering
University at Buffalo
- 1985
MS, Institute of Remote Sensing Applications
Chinese Academy of Sciences
- 1982
BE, Electrical Engineering
Xidian University
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