William Hsu

4.1k total citations
188 papers, 2.5k citations indexed

About

William Hsu is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, William Hsu has authored 188 papers receiving a total of 2.5k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Artificial Intelligence, 62 papers in Radiology, Nuclear Medicine and Imaging and 41 papers in Pulmonary and Respiratory Medicine. Recurrent topics in William Hsu's work include Radiomics and Machine Learning in Medical Imaging (49 papers), AI in cancer detection (31 papers) and Lung Cancer Diagnosis and Treatment (18 papers). William Hsu is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (49 papers), AI in cancer detection (31 papers) and Lung Cancer Diagnosis and Treatment (18 papers). William Hsu collaborates with scholars based in United States, Taiwan and Germany. William Hsu's co-authors include Alex Bui, Denise R. Aberle, Shiwen Shen, Simon Han, Xiaodong Xie, Meng Li, Jason Cong, Wen Gao, Ricky K. Taira and Fei‐Yuan Hsiao and has published in prestigious journals such as Circulation, Bioinformatics and Gastroenterology.

In The Last Decade

William Hsu

168 papers receiving 2.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
William Hsu United States 24 794 714 561 280 260 188 2.5k
Shijun Wang United States 19 790 1.0× 451 0.6× 499 0.9× 412 1.5× 192 0.7× 65 1.8k
Charles E. Kahn United States 30 1.8k 2.3× 453 0.6× 992 1.8× 277 1.0× 656 2.5× 210 3.8k
Luca Saba Italy 28 762 1.0× 538 0.8× 437 0.8× 281 1.0× 73 0.3× 67 1.9k
Noah Simon United States 22 366 0.5× 602 0.8× 678 1.2× 235 0.8× 1.2k 4.6× 77 4.5k
Jun Xia China 25 2.0k 2.5× 464 0.6× 1.2k 2.1× 378 1.4× 523 2.0× 124 4.0k
Mattias Ohlsson Sweden 31 632 0.8× 406 0.6× 428 0.8× 114 0.4× 528 2.0× 152 2.9k
Brandon D. Gallas United States 21 839 1.1× 517 0.7× 425 0.8× 121 0.4× 178 0.7× 83 1.7k
Seong K. Mun United States 24 1.2k 1.5× 715 1.0× 826 1.5× 506 1.8× 119 0.5× 239 2.9k
Paolo Soda Italy 26 593 0.7× 197 0.3× 603 1.1× 434 1.6× 276 1.1× 151 2.1k
Claire Cui United States 4 742 0.9× 175 0.2× 921 1.6× 229 0.8× 204 0.8× 9 2.4k

Countries citing papers authored by William Hsu

Since Specialization
Citations

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

Fields of papers citing papers by William Hsu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of William Hsu

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

All Works

20 of 20 papers shown
1.
Kim, Daniel H., Ida Sonni, Tristan Grogan, et al.. (2025). Quantitative 3-T Multiparametric MRI Parameters as Predictors of Aggressive Prostate Cancer. Radiology Imaging Cancer. 7(1). e240011–e240011. 1 indexed citations
2.
Hoffman, John M., Hyun Woo Kim, Matthew S. Brown, et al.. (2025). A comparative analysis of image harmonization techniques in mitigating differences in CT acquisition and reconstruction. Physics in Medicine and Biology. 70(5). 55015–55015. 2 indexed citations
3.
Zhuang, Luoting, et al.. (2025). Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data. IEEE Reviews in Biomedical Engineering. 19. 182–200.
4.
Chalfant, James S, Lucy Chow, Christopher Sears, et al.. (2025). Mammographic classification of interval breast cancers and artificial intelligence performance. JNCI Journal of the National Cancer Institute. 117(8). 1627–1638. 5 indexed citations
5.
6.
Shamir, Lior, et al.. (2024). Data Science (Dataying) for Early Childhood. 1 indexed citations
7.
Zhang, Tengyue, et al.. (2024). Refining boundaries of the segment anything model in medical images using an active contour model. 115–115. 1 indexed citations
8.
Du, Zhenjiao, Xingjian Ding, William Hsu, et al.. (2023). pLM4ACE: A protein language model based predictor for antihypertensive peptide screening. Food Chemistry. 431. 137162–137162. 50 indexed citations
9.
Hsu, William, et al.. (2023). Multi-Horizon Learning in Procedurally-Generated Environments for Off-Policy Reinforcement Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence. 37(13). 16150–16151. 2 indexed citations
10.
Hsu, William, et al.. (2023). Towards a framework for interoperability and reproducibility of predictive models. Journal of Biomedical Informatics. 149. 104551–104551. 5 indexed citations
11.
Liang, Li‐Jung, et al.. (2023). Factors Associated With Nonadherence to Lung Cancer Screening Across Multiple Screening Time Points. JAMA Network Open. 6(5). e2315250–e2315250. 8 indexed citations
12.
Prosper, Ashley E., Michael N. Kammer, Fabien Maldonado, Denise R. Aberle, & William Hsu. (2023). Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization. Radiology. 309(1). e222904–e222904. 16 indexed citations
13.
Martin, Caleb D., et al.. (2022). KDDIE at SemEval-2022 Task 11: Using DeBERTa for Named Entity Recognition. 1531–1535. 3 indexed citations
14.
Li, Beibin, et al.. (2022). Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification. Computers in Biology and Medicine. 146. 105504–105504. 3 indexed citations
15.
Li, Meng, William Hsu, Xiaodong Xie, Jason Cong, & Wen Gao. (2020). SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network. IEEE Transactions on Medical Imaging. 39(7). 2289–2301. 221 indexed citations
16.
Gao, Yu, Anusha Kalbasi, William Hsu, et al.. (2020). Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs. Physics in Medicine and Biology. 65(17). 175006–175006. 43 indexed citations
17.
Winter, Audrey, et al.. (2019). Using Sequential Decision Making to Improve Lung Cancer Screening Performance. IEEE Access. 7. 119403–119419. 39 indexed citations
18.
Winter, Audrey, Denise R. Aberle, & William Hsu. (2019). External validation and recalibration of the Brock model to predict probability of cancer in pulmonary nodules using NLST data. Thorax. 74(6). 551–563. 20 indexed citations
19.
Hsu, William, Xinkai Zhou, Stephanie Lee‐Felker, et al.. (2018). Role of Clinical and Imaging Risk Factors in Predicting Breast Cancer Diagnosis Among BI-RADS 4 Cases. Clinical Breast Cancer. 19(1). e142–e151. 10 indexed citations
20.
Hsu, William, Nestor R. Gonzalez, Aichi Chien, et al.. (2015). An integrated, ontology-driven approach to constructing observational databases for research. Journal of Biomedical Informatics. 55. 132–142. 19 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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