Wei Mu
Impact in
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Health Informatics top 2%
Papers in
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- Radiomics and Machine Learning in Medical Imaging 21
- Medical Imaging Techniques and Applications 7
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- Nanowire Synthesis and Applications 7
- Co-authors
- Robert J. Gillies (15 shared papers)Xiaojun Han (14 shared papers)Matthew B. Schabath (10 shared papers)Jie Tian (17 shared papers)Ilke Tunali (7 shared papers)Haoyi Wang (3 shared papers)Na Li (2 shared papers)Xingying Zhang (2 shared papers)
- Journals
- Blood (3 papers)Frontiers in Immunology (3 papers)Theranostics (3 papers)IEEE Transactions on Biomedical Engineering (2 papers)Nature Communications (2 papers)
- Partner nations
- ChinaUnited StatesSweden
In The Last Decade
Wei Mu
84 papers receiving 2.9k citations
Wei Mu's Hit Papers
Peers
Comparison fields: 5 of 127
- Radiology, Nuclear Medicine and Imaging 1.0k
- Health Informatics 58
- Oncology 1.0k
- Immunology 273
- Pulmonary and Respiratory Medicine 359
Countries citing papers authored by Wei Mu
This map shows the geographic impact of Wei Mu'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 Wei Mu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wei Mu more than expected).
Fields of papers citing papers by Wei Mu
This network shows the impact of papers produced by Wei Mu. 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 Wei Mu. The network helps show where Wei Mu may publish in the future.
Co-authors
The 25 scholars most cited alongside Wei Mu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 91 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | TGF-β inhibition via CRISPR promotes the long-term efficacy of CAR T cells against solid tumors Hit paper breakdown → | 2020 | 304 |
| 2 | 2020 | 247 | |
| 3 | 2017 | 193 | |
| 4 | 2020 | 192 | |
| 5 | Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images Hit paper breakdown → | 2021 | 156 |
| 6 | 2018 | 152 | |
| 7 | 2019 | 138 | |
| 8 | 2021 | 137 | |
| 9 | 2020 | 115 | |
| 10 | 2019 | 76 | |
| 11 | 2020 | 70 | |
| 12 | 2015 | 70 | |
| 13 | 2022 | 68 | |
| 14 | 2019 | 58 | |
| 15 | 2019 | 56 | |
| 16 | 2024 | 53 | |
| 17 | 2021 | 47 | |
| 18 | 2021 | 41 | |
| 19 | 2022 | 38 | |
| 20 | 2019 | 37 |
About Wei Mu
Wei Mu is a scholar working on Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Oncology, Molecular Biology and Electrical and Electronic Engineering, having authored 91 papers that have together received 2.9k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (21 papers), CAR-T cell therapy research (16 papers), Nanowire Synthesis and Applications (7 papers), Medical Imaging Techniques and Applications (7 papers), Lung Cancer Diagnosis and Treatment (6 papers), Photoreceptor and optogenetics research (5 papers), Medical Image Segmentation Techniques (4 papers) and Hepatocellular Carcinoma Treatment and Prognosis (4 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (1.0k citations), Health Informatics (58 citations), Oncology (1.0k citations), Immunology (273 citations) and Pulmonary and Respiratory Medicine (359 citations). Wei Mu has collaborated with scholars based in China, United States and Sweden. Frequent co-authors include Robert J. Gillies, Xiaojun Han, Matthew B. Schabath, Jie Tian, Ilke Tunali, Haoyi Wang, Na Li, Xingying Zhang, Haolong Lin and Jianfeng Zhou. Their work appears in journals such as Blood, Frontiers in Immunology, Theranostics, IEEE Transactions on Biomedical Engineering and Nature Communications.
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.