Yuan‐Hsiang Chang

51 total papers · 637 total citations
41 papers, 437 citations indexed

About

Yuan‐Hsiang Chang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Yuan‐Hsiang Chang has authored 41 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 15 papers in Computer Vision and Pattern Recognition and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Yuan‐Hsiang Chang's work include AI in cancer detection (21 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Cell Image Analysis Techniques (8 papers). Yuan‐Hsiang Chang is often cited by papers focused on AI in cancer detection (21 papers), Radiomics and Machine Learning in Medical Imaging (10 papers) and Cell Image Analysis Techniques (8 papers). Yuan‐Hsiang Chang collaborates with scholars based in United States, Taiwan and Japan. Yuan‐Hsiang Chang's co-authors include David Gur, Bin Zheng, Walter F. Good, Thomas Chang, Lara A. Hardesty, William R. Poller, Xiao Hui Wang, Xiaohui Wang, Hideo Yokota and Kuniya Abe and has published in prestigious journals such as SHILAP Revista de lepidopterología, American Journal of Roentgenology and Medical Physics.

In The Last Decade

Yuan‐Hsiang Chang

40 papers receiving 424 citations

Author Peers

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

Author Last Decade Papers Cites
Yuan‐Hsiang Chang 327 157 128 104 104 41 437
Deborah Thompson 233 0.7× 109 0.7× 131 1.0× 66 0.6× 58 0.6× 30 466
Xiaosong Rao 297 0.9× 234 1.5× 124 1.0× 102 1.0× 59 0.6× 14 484
Yi‐Jia Lin 225 0.7× 168 1.1× 52 0.4× 56 0.5× 82 0.8× 30 493
Günter Schmidt 111 0.3× 143 0.9× 115 0.9× 52 0.5× 132 1.3× 28 420
Michael Gadermayr 231 0.7× 149 0.9× 156 1.2× 36 0.3× 54 0.5× 36 452
Weihao Xie 93 0.3× 111 0.7× 113 0.9× 58 0.6× 78 0.8× 24 394
Tomoharu Kiyuna 162 0.5× 138 0.9× 57 0.4× 49 0.5× 85 0.8× 30 401
Sanghoon Lee 189 0.6× 76 0.5× 68 0.5× 27 0.3× 47 0.5× 45 369
Mikhail Teverovskiy 319 1.0× 109 0.7× 196 1.5× 105 1.0× 43 0.4× 13 459
Maria Gabrani 248 0.8× 191 1.2× 163 1.3× 29 0.3× 51 0.5× 39 459

Countries citing papers authored by Yuan‐Hsiang Chang

Since Specialization
Citations

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

Fields of papers citing papers by Yuan‐Hsiang Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yuan‐Hsiang Chang. 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 Yuan‐Hsiang Chang. The network helps show where Yuan‐Hsiang Chang may publish in the future.

Co-authorship network of co-authors of Yuan‐Hsiang Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Yuan‐Hsiang Chang. A scholar is included among the top collaborators of Yuan‐Hsiang Chang 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 Yuan‐Hsiang Chang. Yuan‐Hsiang Chang 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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