Shih-Cheng Huang
- Radiology, Nuclear Medicine and Imaging top 2%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Health Informatics top 0.5%
- Pulmonary and Respiratory Medicine
- Co-authors
- Matthew P. LungrenAnuj PareekImon BanerjeeSaeed SeyyediSerena YeungLiyue ShenRoham T. ZamanianAkshay Chaudhari
- Topics
- Radiomics and Machine Learning in Medical Imaging (7 papers)AI in cancer detection (5 papers)COVID-19 diagnosis using AI (4 papers)
- Partner nations
- United StatesCanadaTaiwan
In The Last Decade
Shih-Cheng Huang
21 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 123
- Radiology, Nuclear Medicine and Imaging 589
- Artificial Intelligence 535
- Computer Vision and Pattern Recognition 184
- Health Informatics 177
- Pulmonary and Respiratory Medicine 148
Countries citing papers authored by Shih-Cheng Huang
This map shows the geographic impact of Shih-Cheng Huang'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 Shih-Cheng Huang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shih-Cheng Huang more than expected).
Fields of papers citing papers by Shih-Cheng Huang
This network shows the impact of papers produced by Shih-Cheng Huang. 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 Shih-Cheng Huang. The network helps show where Shih-Cheng Huang may publish in the future.
Co-authorship network of co-authors of Shih-Cheng Huang
This figure shows the co-authorship network connecting the top 25 collaborators of Shih-Cheng Huang. A scholar is included among the top collaborators of Shih-Cheng Huang 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 Shih-Cheng Huang. Shih-Cheng Huang 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 | 1 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | Self-supervised learning for medical image classification: a systematic review and implementation guidelinesbreakdown → | 174 |
| 6 | Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imagingbreakdown → | 89 |
| 7 | 18 | |
| 8 | 12 | |
| 9 | 3 | |
| 10 | 184 | |
| 11 | 25 | |
| 12 | 103 | |
| 13 | Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelinesbreakdown → | 448 |
| 14 | 146 | |
| 15 | 1 | |
| 16 | 1 | |
| 17 | 5 | |
| 18 | 2 | |
| 19 | 3 | |
| 20 | 1 |
About Shih-Cheng Huang
Shih-Cheng Huang is a scholar working on Health Informatics, Internal Medicine and Radiology, Nuclear Medicine and Imaging, having authored 22 papers that have together received 1.3k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (7 papers), AI in cancer detection (5 papers) and COVID-19 diagnosis using AI (4 papers). The work is most often cited by research in Health Informatics (177 citations), Internal Medicine (99 citations) and Radiology, Nuclear Medicine and Imaging (589 citations). Shih-Cheng Huang has collaborated with scholars based in United States, Canada and Taiwan. Frequent co-authors include Matthew P. Lungren, Anuj Pareek, Imon Banerjee, Saeed Seyyedi, Serena Yeung, Liyue Shen, Roham T. Zamanian, Akshay Chaudhari, Malte Jensen and Rui Yan. Their work appears in journals such as Nature Communications, Journal of Clinical Oncology and Applied Physics Letters.
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.