Changfa Shi
Impact in
-
- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
-
- Medical Image Segmentation Techniques 6
- Advanced Neural Network Applications 5
-
- Radiomics and Machine Learning in Medical Imaging 3
- Retinal Imaging and Analysis 2
- COVID-19 diagnosis using AI 2
- Co-authors
- Jinke Wang (11 shared papers)Haiying Wang (3 shared papers)Shinichi Tamura (4 shared papers)Yadong Wang (2 shared papers)Yuanzhi Cheng (3 shared papers)Fei Liu (1 shared paper)Jing Bai (1 shared paper)Kensaku Mori (1 shared paper)
- Journals
- Mathematical Biosciences & Engineering (2 papers)Medical Image Analysis (2 papers)Sustainability (1 paper)Computer Methods and Programs in Biomedicine (1 paper)Pattern Recognition (1 paper)
- Partner nations
- ChinaJapanUnited States
In The Last Decade
Changfa Shi
15 papers receiving 306 citations
Peers
Comparison fields: 5 of 65
- Computer Vision and Pattern Recognition 142
- Radiology, Nuclear Medicine and Imaging 125
- Neurology 40
- Computational Mathematics 2
- Artificial Intelligence 83
Countries citing papers authored by Changfa Shi
This map shows the geographic impact of Changfa Shi'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 Changfa Shi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Changfa Shi more than expected).
Fields of papers citing papers by Changfa Shi
This network shows the impact of papers produced by Changfa Shi. 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 Changfa Shi. The network helps show where Changfa Shi may publish in the future.
Co-authors
The 16 scholars most cited alongside Changfa Shi, 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 | 2021 | 116 | |
| 2 | 2017 | 60 | |
| 3 | 2015 | 57 | |
| 4 | 2017 | 18 | |
| 5 | 2021 | 15 | |
| 6 | 2022 | 11 | |
| 7 | 2022 | 10 | |
| 8 | 2021 | 10 | |
| 9 | 2023 | 5 | |
| 10 | 2024 | 2 | |
| 11 | 2024 | 2 | |
| 12 | 2014 | 2 | |
| 13 | SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver CT segmentation. | 2021 | 1 |
| 14 | 2023 | 1 | |
| 15 | 2019 | 1 |
About Changfa Shi
Changfa Shi is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Artificial Intelligence and Strategy and Management, having authored 15 papers that have together received 311 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (6 papers), Advanced Neural Network Applications (5 papers), Medical Imaging and Analysis (4 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Retinal Imaging and Analysis (2 papers), COVID-19 diagnosis using AI (2 papers), AI in cancer detection (2 papers) and Qualitative Comparative Analysis Research (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (142 citations), Radiology, Nuclear Medicine and Imaging (125 citations), Neurology (40 citations), Computational Mathematics (2 citations) and Artificial Intelligence (83 citations). Changfa Shi has collaborated with scholars based in China, Japan and United States. Frequent co-authors include Jinke Wang, Haiying Wang, Shinichi Tamura, Yadong Wang, Yuanzhi Cheng, Fei Liu, Jing Bai, Kensaku Mori, Xiangyang Zhang and Min Xian. Their work appears in journals such as Mathematical Biosciences & Engineering, Medical Image Analysis, Sustainability, Computer Methods and Programs in Biomedicine and Pattern Recognition.
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.