Stephen Seiler

753 total citations
43 papers, 450 citations indexed

About

Stephen Seiler is a scholar working on Radiology, Nuclear Medicine and Imaging, Cancer Research and Pathology and Forensic Medicine. According to data from OpenAlex, Stephen Seiler has authored 43 papers receiving a total of 450 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Cancer Research and 13 papers in Pathology and Forensic Medicine. Recurrent topics in Stephen Seiler's work include Breast Cancer Treatment Studies (15 papers), Breast Lesions and Carcinomas (11 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Stephen Seiler is often cited by papers focused on Breast Cancer Treatment Studies (15 papers), Breast Lesions and Carcinomas (11 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Stephen Seiler collaborates with scholars based in United States, Germany and China. Stephen Seiler's co-authors include Sally Goudreau, Xuejun Gu, Weiguo Lu, Erlei Zhang, Jochen Keupp, Elena Vinogradov, Ramapriya Ganti, Robert E. Lenkinski, Helena Hwang and Sunati Sahoo and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Stephen Seiler

37 papers receiving 440 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Stephen Seiler United States 10 231 165 146 94 69 43 450
Akiko Shimauchi United States 16 641 2.8× 214 1.3× 289 2.0× 84 0.9× 111 1.6× 31 820
D J Manton United Kingdom 13 601 2.6× 123 0.7× 80 0.5× 47 0.5× 26 0.4× 16 739
L. M. Porfiri Italy 12 244 1.1× 75 0.5× 144 1.0× 49 0.5× 24 0.3× 21 412
Christiane Marx Germany 14 381 1.6× 96 0.6× 187 1.3× 36 0.4× 146 2.1× 18 656
Sally Goudreau United States 8 99 0.4× 119 0.7× 77 0.5× 35 0.4× 20 0.3× 20 263
John A. Cutrone United States 7 373 1.6× 80 0.5× 149 1.0× 24 0.3× 14 0.2× 13 448
Belinda Curpen Canada 17 478 2.1× 227 1.4× 161 1.1× 24 0.3× 274 4.0× 49 837
Su Min Ha South Korea 16 450 1.9× 302 1.8× 223 1.5× 220 2.3× 137 2.0× 59 924
P. Viehweg Germany 13 796 3.4× 204 1.2× 519 3.6× 34 0.4× 102 1.5× 19 959
Elizabeth R. DePeri United States 11 598 2.6× 411 2.5× 510 3.5× 78 0.8× 73 1.1× 13 992

Countries citing papers authored by Stephen Seiler

Since Specialization
Citations

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

Fields of papers citing papers by Stephen Seiler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stephen Seiler

This figure shows the co-authorship network connecting the top 25 collaborators of Stephen Seiler. A scholar is included among the top collaborators of Stephen Seiler 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 Stephen Seiler. Stephen Seiler 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.
Wu, D. Y., Yisheng Fang, Dat T. Vo, Ann Spangler, & Stephen Seiler. (2024). Detailed Image Data Quality and Cleaning Practices for Artificial Intelligence Tools for Breast Cancer. JCO Clinical Cancer Informatics. 8(8). e2300074–e2300074. 1 indexed citations
2.
Wu, D. Y., Dat T. Vo, & Stephen Seiler. (2024). Opinion: Big Data Elements Key to Medical Imaging Machine Learning Tool Development. Journal of Breast Imaging. 6(2). 217–219.
3.
Doğan, Başak E., et al.. (2023). Breast Cancer Disparity and Outcomes in Underserved Women. Radiographics. 44(1). e230090–e230090. 5 indexed citations
4.
5.
Seiler, Stephen, et al.. (2023). Surviving the COVID-19 pandemic: navigating the recovery of breast imaging services in a safety-net hospital. Breast Cancer Research and Treatment. 201(1). 127–138.
6.
Seiler, Stephen, et al.. (2022). Radial Scars Without Atypia Diagnosed at Percutaneous Core Needle Breast Biopsy: Support for Imaging Surveillance. SHILAP Revista de lepidopterología. 19(1). 76–84. 3 indexed citations
7.
Goudreau, Sally, et al.. (2021). Multimodality Review of Imaging Features Following Breast Reduction Surgery. SHILAP Revista de lepidopterología. 17(3). 206–213. 6 indexed citations
8.
Seiler, Stephen, et al.. (2020). Neoplastic seeding of breast cancer along the core biopsy tract. The Breast Journal. 26(10). 2129–2131. 2 indexed citations
9.
Ganti, Ramapriya, et al.. (2020). Benign breast papillomas without atypia diagnosed with core needle biopsy: Outcome of surgical excision and imaging follow-up. European Journal of Radiology. 131. 109237–109237. 9 indexed citations
10.
Zhang, Erlei, et al.. (2020). BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis. Physics in Medicine and Biology. 65(12). 125005–125005. 43 indexed citations
11.
Wu, D. Y., Alberto de Hoyos, Dat T. Vo, et al.. (2020). Clinical Non–Small Cell Lung Cancer Staging and Tumor Length Measurement Results From U.S. Cancer Hospitals. Academic Radiology. 28(6). 753–766. 8 indexed citations
12.
Wu, D. Y., Ann Spangler, Dat T. Vo, Alberto de Hoyos, & Stephen Seiler. (2020). Simplified, standardized methods to assess the accuracy of clinical cancer staging. Cancer Treatment and Research Communications. 25. 100253–100253. 1 indexed citations
13.
Mokdad, Ali H., Nancy Puzziferri, Stephen Seiler, et al.. (2018). Mammographic density changes in surgical weight loss-an indication for personalized screening. BMC Medical Imaging. 18(1). 10–10. 7 indexed citations
14.
Rahimi, Asal, K. Thomas, Ann Spangler, et al.. (2017). Preliminary Results of a Phase 1 Dose-Escalation Trial for Early-Stage Breast Cancer Using 5-Fraction Stereotactic Body Radiation Therapy for Partial-Breast Irradiation. International Journal of Radiation Oncology*Biology*Physics. 98(1). 196–205.e2. 49 indexed citations
15.
Seiler, Stephen, et al.. (2017). Magnetic resonance imaging characteristics of granulomatous mastitis. Clinical Imaging. 43. 199–201. 18 indexed citations
16.
Slavine, N., Stephen Seiler, Roderick McColl, & Robert E. Lenkinski. (2017). Image improvement method for positron emission mammography. Physica Medica. 39. 164–173.
17.
Lynch, Beverly J., et al.. (2017). Pathologic Findings of Breast Lesions Detected on Magnetic Resonance Imaging. Archives of Pathology & Laboratory Medicine. 141(11). 1513–1522. 21 indexed citations
18.
Goudreau, Sally, et al.. (2015). Preoperative Radioactive Seed Localization for Nonpalpable Breast Lesions: Technique, Pitfalls, and Solutions. Radiographics. 35(5). 1319–1334. 69 indexed citations
19.
By, Samantha, Joseph V. Rispoli, Sergey Cheshkov, et al.. (2014). A 16-Channel Receive, Forced Current Excitation Dual-Transmit Coil for Breast Imaging at 7T. PLoS ONE. 9(11). e113969–e113969. 16 indexed citations
20.
Seiler, Stephen & Robert E. Lenkinski. (2012). Dedicated PET device for breast PET and MRI/PET correlations. European Journal of Radiology. 81. S149–S150. 1 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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