Wei-Hsiang Yu

930 total citations
10 papers, 268 citations indexed

About

Wei-Hsiang Yu is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Wei-Hsiang Yu has authored 10 papers receiving a total of 268 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Artificial Intelligence and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Wei-Hsiang Yu's work include Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (6 papers) and Lung Cancer Diagnosis and Treatment (4 papers). Wei-Hsiang Yu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (9 papers), AI in cancer detection (6 papers) and Lung Cancer Diagnosis and Treatment (4 papers). Wei-Hsiang Yu collaborates with scholars based in Taiwan. Wei-Hsiang Yu's co-authors include Chao‐Yuan Yeh, Chi‐Chung Chen, Tai‐I Hsu, Michael Hsiao, Yu‐Chan Chang, Cheng‐Yu Chen, Chi‐Long Chen, Chi‐Ju Yeh, Wen‐Yu Chuang and Chuen Hsueh and has published in prestigious journals such as Nature Communications, American Journal Of Pathology and Modern Pathology.

In The Last Decade

Wei-Hsiang Yu

9 papers receiving 261 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Wei-Hsiang Yu Taiwan	6	179	166	68	50	45	10	268
Juan Antonio Retámero Spain	8	116 0.6×	176 1.1×	71 1.0×	82 1.6×	30 0.7×	13	299
Patricia Raciti United States	6	157 0.9×	204 1.2×	96 1.4×	53 1.1×	17 0.4×	10	299
Peter Truszkowski United States	2	187 1.0×	211 1.3×	50 0.7×	60 1.2×	41 0.9×	2	341
Manuela Vecsler United States	4	179 1.0×	196 1.2×	63 0.9×	45 0.9×	19 0.4×	5	290
Andrea M. Olofson United States	7	182 1.0×	186 1.1×	41 0.6×	124 2.5×	47 1.0×	13	324
Kyunghyun Paeng South Korea	9	136 0.8×	104 0.6×	58 0.9×	92 1.8×	27 0.6×	29	237
Rebecca Batiste United States	5	132 0.7×	114 0.7×	53 0.8×	49 1.0×	35 0.8×	7	265
Charles Maussion France	4	178 1.0×	190 1.1×	74 1.1×	67 1.3×	34 0.8×	12	338
Nick Weiss Germany	10	151 0.8×	176 1.1×	45 0.7×	49 1.0×	104 2.3×	16	353
Nicolas Brieu Germany	11	120 0.7×	93 0.6×	45 0.7×	121 2.4×	28 0.6×	27	273

Countries citing papers authored by Wei-Hsiang Yu

Since Specialization

Citations

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

Fields of papers citing papers by Wei-Hsiang Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wei-Hsiang Yu. 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 Wei-Hsiang Yu. The network helps show where Wei-Hsiang Yu may publish in the future.

Co-authorship network of co-authors of Wei-Hsiang Yu

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

Lin, Shinn‐Zong, Jia‐Ru Wu, Wei-Hsiang Yu, et al.. (2024). Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features in Digital Pathological Images of Common Astrocytic Tumors. Cancers. 16(13). 2449–2449.

Chuang, Wen‐Yu, Wei-Hsiang Yu, Hung Chang, et al.. (2022). Deep Learning–Based Nuclear Morphometry Reveals an Independent Prognostic Factor in Mantle Cell Lymphoma. American Journal Of Pathology. 192(12). 1763–1778. 3 indexed citations

Wang, Ren‐Ching, Bo‐Jung Chen, Chang‐Tsu Yuan, et al.. (2022). The spectrum of intestinal mature T- and NK-cell neoplasms in a tertiary center in Taiwan with a high frequency of perforation. Pathology - Research and Practice. 240. 154184–154184. 2 indexed citations

Yu, Wei-Hsiang, Chih‐Hao Li, Ren‐Ching Wang, Chao‐Yuan Yeh, & Shih‐Sung Chuang. (2021). Machine Learning Based on Morphological Features Enables Classification of Primary Intestinal T-Cell Lymphomas. Cancers. 13(21). 5463–5463. 14 indexed citations

Chuang, Wen‐Yu, Chi‐Chung Chen, Wei-Hsiang Yu, et al.. (2021). Identification of nodal micrometastasis in colorectal cancer using deep learning on annotation-free whole-slide images. Modern Pathology. 34(10). 1901–1911. 38 indexed citations

Chen, Chi‐Long, Chi‐Chung Chen, Wei-Hsiang Yu, et al.. (2021). An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nature Communications. 12(1). 1193–1193. 144 indexed citations

Yeh, Chao‐Yuan, Wei-Hsiang Yu, Chengkun Yang, et al.. (2021). Predicting aggressive histopathological features in esophageal cancer with positron emission tomography using a deep convolutional neural network. Annals of Translational Medicine. 9(1). 37–37. 7 indexed citations

Chen, Chi‐Chung, Wei-Hsiang Yu, & Chao‐Yuan Yeh. (2021). An Annotation-free Whole-slide Training Approach to Pathological Classification of Lung Cancer Types by Deep Learning, whole-slide-cnn. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

Chuang, Wen‐Yu, Shang‐Hung Chang, Wei-Hsiang Yu, et al.. (2020). Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning. Cancers. 12(2). 507–507. 37 indexed citations

10.

Yang, Chengkun, et al.. (2019). Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome. Journal of Clinical Medicine. 8(6). 844–844. 22 indexed citations

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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