Peng Tang

40 total papers · 1.0k total citations
22 papers, 582 citations indexed

About

Peng Tang is a scholar working on Computer Vision and Pattern Recognition, Oncology and Artificial Intelligence. According to data from OpenAlex, Peng Tang has authored 22 papers receiving a total of 582 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Oncology and 6 papers in Artificial Intelligence. Recurrent topics in Peng Tang's work include Cutaneous Melanoma Detection and Management (6 papers), AI in cancer detection (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Peng Tang is often cited by papers focused on Cutaneous Melanoma Detection and Management (6 papers), AI in cancer detection (5 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Peng Tang collaborates with scholars based in China, Germany and Canada. Peng Tang's co-authors include Shao Xiang, Xintong Yan, Qiaokang Liang, Dan Zhang, Gianmarc Coppola, Wei Sun, Nan Yang, Sebastian Krammer, Tobias Lasser and Mi Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Medical Imaging and Pattern Recognition.

In The Last Decade

Peng Tang

22 papers receiving 568 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Peng Tang	279	273	152	118	109	22	582
Muhammad Rashid	281 1.0×	298 1.1×	259 1.7×	80 0.7×	124 1.1×	14	677
Aledir Silveira Pereira	312 1.1×	383 1.4×	194 1.3×	124 1.1×	103 0.9×	24	660
F. Joel W.-M. Leong	191 0.7×	77 0.3×	95 0.6×	39 0.3×	106 1.0×	23	533
Mohammad H. Jafari	301 1.1×	334 1.2×	151 1.0×	115 1.0×	191 1.8×	26	660
Ebrahim Nasr-Esfahani	262 0.9×	289 1.1×	157 1.0×	93 0.8×	143 1.3×	11	533
P. C. Siddalingaswamy	221 0.8×	240 0.9×	163 1.1×	72 0.6×	215 2.0×	34	530
Roberta B. Oliveira	290 1.0×	385 1.4×	125 0.8×	122 1.0×	48 0.4×	22	538
Tim Lee	166 0.6×	315 1.2×	59 0.4×	89 0.8×	33 0.3×	28	677
Rusong Meng	233 0.8×	355 1.3×	79 0.5×	122 1.0×	48 0.4×	29	578
Ahmad Naeem	374 1.3×	318 1.2×	89 0.6×	151 1.3×	185 1.7×	23	654

Countries citing papers authored by Peng Tang

Since Specialization

Citations

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

Fields of papers citing papers by Peng Tang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peng Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Peng Tang. A scholar is included among the top collaborators of Peng Tang 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 Peng Tang. Peng Tang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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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