Kai Han
Impact in
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- Advanced Neural Network Applications
- Medical Image Segmentation Techniques
Papers in
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- Advanced Neural Network Applications 10
- Medical Image Segmentation Techniques 5
- Multimodal Machine Learning Applications 2
-
- AI in cancer detection 4
- Domain Adaptation and Few-Shot Learning 3
- Co-authors
- Zhe Liu (13 shared papers)Yuqing Song (8 shared papers)Victor S. Sheng (7 shared papers)Yang Lu (1 shared paper)Kenneth K. Wong (2 shared papers)Ying Shan (1 shared paper)Yan‐Pei Cao (1 shared paper)Yi Liu (4 shared papers)
- Journals
- Multimedia Systems (4 papers)Biomedical Signal Processing and Control (2 papers)Expert Systems with Applications (2 papers)IEEE Transactions on Medical Imaging (1 paper)Science China Information Sciences (1 paper)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Kai Han
19 papers receiving 341 citations
Kai Han's Hit Papers
Peers
Comparison fields: 5 of 78
- Computer Vision and Pattern Recognition 174
- Health Informatics 11
- Computer Graphics and Computer-Aided Design 24
- Neurology 47
- Radiology, Nuclear Medicine and Imaging 110
Countries citing papers authored by Kai Han
This map shows the geographic impact of Kai Han'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 Kai Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai Han more than expected).
Fields of papers citing papers by Kai Han
This network shows the impact of papers produced by Kai Han. 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 Kai Han. The network helps show where Kai Han may publish in the future.
Co-authors
The 25 scholars most cited alongside Kai Han, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 92 | |
| 2 | Deep semi-supervised learning for medical image segmentation: A review Hit paper breakdown → | 2024 | 75 |
| 3 | 2024 | 39 | |
| 4 | 2022 | 30 | |
| 5 | 2020 | 24 | |
| 6 | 2022 | 17 | |
| 7 | 2022 | 15 | |
| 8 | 2022 | 15 | |
| 9 | 2023 | 8 | |
| 10 | 2025 | 8 | |
| 11 | 2022 | 7 | |
| 12 | 2023 | 6 | |
| 13 | 2024 | 5 | |
| 14 | 2020 | 3 | |
| 15 | 2024 | 2 | |
| 16 | 2022 | 1 | |
| 17 | 2014 | 1 | |
| 18 | 2023 | 1 | |
| 19 | 2023 | 1 | |
| 20 | 2025 | 0 |
About Kai Han
Kai Han is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computational Mechanics and Biomedical Engineering, having authored 20 papers that have together received 350 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (10 papers), COVID-19 diagnosis using AI (6 papers), Medical Image Segmentation Techniques (5 papers), AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Domain Adaptation and Few-Shot Learning (3 papers), Multimodal Machine Learning Applications (2 papers) and 3D Shape Modeling and Analysis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (174 citations), Health Informatics (11 citations), Computer Graphics and Computer-Aided Design (24 citations), Neurology (47 citations) and Radiology, Nuclear Medicine and Imaging (110 citations). Kai Han has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Zhe Liu, Yuqing Song, Victor S. Sheng, Yang Lu, Kenneth K. Wong, Ying Shan, Yan‐Pei Cao, Yi Liu, Yan Zhu and Guanying Chen. Their work appears in journals such as Multimedia Systems, Biomedical Signal Processing and Control, Expert Systems with Applications, IEEE Transactions on Medical Imaging and Science China Information Sciences.
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.