Xiaomeng Shi

481 citations
20 papers · 318 indexed · 1 hit paper · h-index 10

Xiaomeng Shi

20 papers receiving 310 citations

Hit Papers

Deep Learning Reconstruction Shows Better Lung Nodule Det...1182022202620232024255075100

Peers

Xiaomeng Shi
Comparison fields: 5 of 70
  • Radiology, Nuclear Medicine and Imaging 214
  • Health Informatics 5
  • Biomedical Engineering 156
  • Pulmonary and Respiratory Medicine 71
  • Structural Biology 2
Replace Yacheng Ren with:
Yacheng Ren China
Hyun Keol Kim United States
Seiichi Murakami Japan
Xing Gong United States
Annika M. K. Nilsson Sweden
Venkataramanan Krishnaswamy United States
Natsuki Shimizu Japan
Roman Wesołowski United Kingdom
Jiping Yan China
Cynthia Araujo Canada
Xiaomeng Shi relative to Yacheng Ren China Yacheng Ren's profile →
Citations per field
00.5×4.8×
Yacheng Ren · 1×
Citations per year

Countries citing papers authored by Xiaomeng Shi

Since Specialization
Citations

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

Fields of papers citing papers by Xiaomeng Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20253
2 20246
3 20245
4 20242
5 20241
6 20244
7 20239
8 202342
9 202310
10 20231
11 20239
12 202314
13 202228
14 202216
15 202224
16
Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra–Low-Dose Chest CTbreakdown →
2022118
17 20221
18 20205
19 201518
20 20152

About Xiaomeng Shi

Xiaomeng Shi is a scholar working on Radiology, Nuclear Medicine and Imaging, Oral Surgery and Biomedical Engineering, having authored 20 papers that have together received 318 indexed citations. Recurring topics across this work include Advanced X-ray and CT Imaging (12 papers), Radiation Dose and Imaging (10 papers), Medical Imaging Techniques and Applications (6 papers), Radiomics and Machine Learning in Medical Imaging (4 papers), Atmospheric and Environmental Gas Dynamics (2 papers), Atmospheric aerosols and clouds (2 papers), Meteorological Phenomena and Simulations (2 papers) and Dental Radiography and Imaging (2 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (214 citations), Health Informatics (5 citations) and Biomedical Engineering (156 citations). Xiaomeng Shi has collaborated with scholars based in China, United Kingdom and Germany. Frequent co-authors include Jianying Li, Xueqian Xie, Rozemarijn Vliegenthart, Shuai Zhang, Geertruida H. de Bock, Beibei Jiang, Weiwu Yao, Jingyu Zhong, Yong Chen and Wei Lu. Their work appears in journals such as European Radiology, Insights into Imaging, Journal of Digital Imaging, Atmosphere 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.

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