Caixia Fu

2.9k citations
142 papers · 2.2k · h-index 24

Impact in

    • MRI in cancer diagnosis
    • Radiomics and Machine Learning in Medical Imaging
    • Advanced MRI Techniques and Applications
    • Advanced Neuroimaging Techniques and Applications
  • Hepatology top 5%
    • Hepatocellular Carcinoma Treatment and Prognosis

Papers in

Caixia Fu

127 papers receiving 2.2k citations

Peers

Caixia Fu
Comparison fields: 5 of 98
  • Radiology, Nuclear Medicine and Imaging 1.1k
  • Hepatology 202
  • Pulmonary and Respiratory Medicine 252
  • Otorhinolaryngology 31
  • Obstetrics and Gynecology 37
Replace Yoshifumi Noda with:
Yoshifumi Noda Japan
Takaya Yamamoto Japan
Yoshihiro Takeda Japan
Hyo Jung Park South Korea
Minglun Li Germany
Kunihiko Yokoyama Japan
Francesco Petrella Italy
Yuting Liao China
Jia Sun United States
Caixia Fu relative to Yoshifumi Noda Japan Yoshifumi Noda's profile →
Citations per field
00.5×3.9×
Yoshifumi Noda · 1×
Citations per year

Countries citing papers authored by Caixia Fu

Since Specialization
Citations

This map shows the geographic impact of Caixia Fu'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 Caixia Fu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Caixia Fu more than expected).

Fields of papers citing papers by Caixia Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Caixia Fu. 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 Caixia Fu. The network helps show where Caixia Fu may publish in the future.

Co-authors

The 25 scholars most cited alongside Caixia Fu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Caixia Fu Line = papers co-authored together Caixia Fu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 142 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015193
2 2017129
3 201688
4 202077
5 201468
6 201867
7 201658
8 202257
9 201756
10 201755
11 201745
12 201843
13 201641
14 202033
15 202030
16 201629
17 201829
18 201628
19 202127
20 201927

About Caixia Fu

Caixia Fu is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Oncology, Hepatology and Epidemiology, having authored 142 papers that have together received 2.2k indexed citations. Recurring topics across this work include MRI in cancer diagnosis (51 papers), Advanced MRI Techniques and Applications (17 papers), Radiomics and Machine Learning in Medical Imaging (15 papers), Hepatocellular Carcinoma Treatment and Prognosis (8 papers), Colorectal Cancer Surgical Treatments (7 papers), Cerebrovascular and Carotid Artery Diseases (7 papers), Liver Disease Diagnosis and Treatment (7 papers) and Prostate Cancer Diagnosis and Treatment (6 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (1.1k citations), Hepatology (202 citations), Pulmonary and Respiratory Medicine (252 citations), Otorhinolaryngology (31 citations) and Obstetrics and Gynecology (37 citations). Caixia Fu has collaborated with scholars based in China, Germany and United States. Frequent co-authors include Mengsu Zeng, Xu Yan, Robert Grimm, Shengxiang Rao, Caizhong Chen, Kun Sun, Yuqin Ding, Jianjun Zhou, Li Yang and Weimin Chai. Their work appears in journals such as Journal of Magnetic Resonance Imaging, European Radiology, European Journal of Radiology, Magnetic Resonance Imaging and Frontiers in Oncology.

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.

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