Peng An

1.2k citations
44 papers · 359 · 1 hit paper · h-index 8

Impact in

Papers in

Peng An

36 papers receiving 350 citations

Peng An's Hit Papers

Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan 2021 · 176 citations
1760+1+3Years since publication50100150

Peers

Peng An
Comparison fields: 5 of 78
  • Health Informatics 48
  • Radiology, Nuclear Medicine and Imaging 161
  • Artificial Intelligence 138
  • Infectious Diseases 61
  • General Dentistry 6
Replace Ali Sabri with:
Ali Sabri Canada
Jocelyn Zhu United States
Sarah Denny United Kingdom
Zhenchao Tang China
Xiaoming Qiu China
Jichan Shi China
Zherui Liu China
Junfeng Li China
Varun Buch United States
Peng An relative to Ali Sabri Canada Ali Sabri's profile →
Citations per field
00.5×1.5×
Ali Sabri · 1×
Citations per year

Countries citing papers authored by Peng An

Since Specialization
Citations

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

Fields of papers citing papers by Peng An

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Peng An, 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 Peng An Line = papers co-authored together Peng An links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan
Hit paper breakdown →
2021176
2 202028
3 202028
4 202018
5 202012
6 20158
7 20237
8 20207
9
[Effect of artemisinin on the expressions of GRalpha mRNA, GRbeta mRNA and P300/CBP protein in lupus nephritis mice].
20127
10 20226
11 20206
12 20196
13 20225
14 20225
15 20234
16 20204
17
Application value of stomach filling ultrasonography and intravenous contrast agents in diagnosis of advanced gastric cancer.
20164
18 20233
19 20223
20 20163

About Peng An

Peng An is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine, Surgery, Oncology and Infectious Diseases, having authored 44 papers that have together received 359 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (12 papers), COVID-19 diagnosis using AI (6 papers), MRI in cancer diagnosis (5 papers), COVID-19 Clinical Research Studies (5 papers), Lung Cancer Diagnosis and Treatment (3 papers), Pancreatic and Hepatic Oncology Research (3 papers), Ultrasound in Clinical Applications (3 papers) and COVID-19 and healthcare impacts (3 papers). The work is most often cited by research in Health Informatics (48 citations), Radiology, Nuclear Medicine and Imaging (161 citations), Artificial Intelligence (138 citations), Infectious Diseases (61 citations) and General Dentistry (6 citations). Peng An has collaborated with scholars based in China, United States and Japan. Frequent co-authors include Bradford J. Wood, Sheng Xu, Evrim Türkbey, Barış Türkbey, Gianpaolo Carrafiello, Kaku Tamura, Andriy Myronenko, Hitoshi Mori, Hirofumi Obinata and Xiaosong Wang. Their work appears in journals such as Technology in Cancer Research & Treatment, International Journal of Clinical Practice, Cancer Imaging, Academic Radiology and Mathematical Biosciences & Engineering.

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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