Yifei Min
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Artificial Intelligence
- Neurology
- Biomedical Engineering
- Co-authors
- Chenyu YouLawrence H. StaibJames S. DuncanFenglin LiuXiaoxiao LiDavid A. CliftonNicha C. DvornekAmin Karbasi
- Topics
- Medical Image Segmentation Techniques (2 papers)AI in cancer detection (2 papers)Medical Imaging and Analysis (2 papers)
- Journals
- Nature CommunicationsIEEE Transactions on Pattern Analysis and Machine IntelligenceSoft Matter
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Yifei Min
7 papers receiving 176 citations
Peers
Comparison fields: 5 of 35
- Computer Vision and Pattern Recognition 101
- Radiology, Nuclear Medicine and Imaging 67
- Artificial Intelligence 64
- Neurology 34
- Biomedical Engineering 29
Countries citing papers authored by Yifei Min
This map shows the geographic impact of Yifei Min'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 Yifei Min with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yifei Min more than expected).
Fields of papers citing papers by Yifei Min
This network shows the impact of papers produced by Yifei Min. 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 Yifei Min. The network helps show where Yifei Min may publish in the future.
Co-authorship network of co-authors of Yifei Min
This figure shows the co-authorship network connecting the top 25 collaborators of Yifei Min. A scholar is included among the top collaborators of Yifei Min 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 Yifei Min. Yifei Min is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 35 | |
| 3 | 35 | |
| 4 | 54 | |
| 5 | 41 | |
| 6 | 0 | |
| 7 | The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization | 4 |
| 8 | More Data Can Expand The Generalization Gap Between Adversarially Robust and Standard Models | 6 |
| 9 | 3 |
About Yifei Min
Yifei Min is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 9 papers that have together received 178 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (2 papers), AI in cancer detection (2 papers) and Medical Imaging and Analysis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (101 citations), Neurology (34 citations) and Radiology, Nuclear Medicine and Imaging (67 citations). Yifei Min has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Chenyu You, Lawrence H. Staib, James S. Duncan, James S. Duncan, Fenglin Liu, Xiaoxiao Li, David A. Clifton, Nicha C. Dvornek, Amin Karbasi and Prashant K. Purohit. Their work appears in journals such as Nature Communications, IEEE Transactions on Pattern Analysis and Machine Intelligence and Soft Matter.
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.