Michael E. Pyle

447 total citations
11 papers, 279 citations indexed

About

Michael E. Pyle is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Cancer Research. According to data from OpenAlex, Michael E. Pyle has authored 11 papers receiving a total of 279 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Cancer Research. Recurrent topics in Michael E. Pyle's work include AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Breast Cancer Treatment Studies (3 papers). Michael E. Pyle is often cited by papers focused on AI in cancer detection (5 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Breast Cancer Treatment Studies (3 papers). Michael E. Pyle collaborates with scholars based in United States, Netherlands and United Kingdom. Michael E. Pyle's co-authors include Yujing J. Heng, Gabrielle M. Baker, Rulla M. Tamimi, Kevin H. Kensler, Amy Hackney, Stuart J. Schnitt, Mitko Veta, Beat Thürlimann, Giuseppe Viale and Marco Colleoni and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and Cancer Research.

In The Last Decade

Michael E. Pyle

10 papers receiving 272 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Michael E. Pyle United States	8	99	77	74	61	53	11	279
Bożena Kukiełka-Budny Poland	5	117 1.2×	43 0.6×	66 0.9×	68 1.1×	19 0.4×	8	266
Anita Turk United States	9	193 1.9×	65 0.8×	55 0.7×	161 2.6×	14 0.3×	40	356
Éric Lifrange Belgium	10	86 0.9×	31 0.4×	141 1.9×	112 1.8×	30 0.6×	27	345
Chloe Chhor United States	9	105 1.1×	200 2.6×	91 1.2×	100 1.6×	131 2.5×	24	440
Hela Koka United States	7	49 0.5×	41 0.5×	58 0.8×	28 0.5×	25 0.5×	16	132
Feng Yao China	12	178 1.8×	23 0.3×	183 2.5×	204 3.3×	15 0.3×	22	468
Vilmar Marques de Oliveira Brazil	11	105 1.1×	16 0.2×	189 2.6×	17 0.3×	43 0.8×	45	385
Francesca D’Avanzo Italy	3	133 1.3×	52 0.7×	58 0.8×	61 1.0×	12 0.2×	8	255
Karleen Habin United States	12	311 3.1×	122 1.6×	156 2.1×	167 2.7×	12 0.2×	27	520
Amanda Kerr United Kingdom	6	104 1.1×	64 0.8×	97 1.3×	73 1.2×	33 0.6×	7	285

Countries citing papers authored by Michael E. Pyle

Since Specialization

Citations

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

Fields of papers citing papers by Michael E. Pyle

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael E. Pyle. 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 Michael E. Pyle. The network helps show where Michael E. Pyle may publish in the future.

Co-authorship network of co-authors of Michael E. Pyle

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

All Works

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11 of 11 papers shown

Nguyen, Tan H., Mohammad Mirzadeh, Aaditya Prakash, et al.. (2023). Abstract P5-02-09: Quantitative analysis of fiber-level collagen features in H&E whole-slide images predicts neoadjuvant therapy response in patients with HER2+ breast cancer. Cancer Research. 83(5_Supplement). P5–2. 1 indexed citations

Pelekanou, Vasiliki, Andreas Schlicker, Ekaterina Nevedomskaya, et al.. (2023). Development of machine learning–powered models for prostate cancer HRD prediction.. Journal of Clinical Oncology. 41(6_suppl). 206–206.

Roberts, Michelle, Gabrielle M. Baker, Yujing J. Heng, et al.. (2021). Reliability of a computational platform as a surrogate for manually interpreted immunohistochemical markers in breast tumor tissue microarrays. Cancer Epidemiology. 74. 101999–101999. 7 indexed citations

Kensler, Kevin H., Gabrielle M. Baker, Andreea Lucia Stancu, et al.. (2020). Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer. JNCI Cancer Spectrum. 5(1). 17 indexed citations

Baker, Gabrielle M., Michael E. Pyle, Kevin H. Kensler, et al.. (2020). Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk. PLoS ONE. 15(4). e0231653–e0231653. 33 indexed citations

Tian, Katherine, Douglas I. Lin, Mitko Veta, et al.. (2019). Automated clear cell renal carcinoma grade classification with prognostic significance. PLoS ONE. 14(10). e0222641–e0222641. 41 indexed citations

Baker, Gabrielle M., Michael E. Pyle, Adam M. Tobias, et al.. (2019). Establishing a Cohort of Transgender Men and Gender Nonconforming Individuals to Understand the Molecular Impact of Testosterone on Breast Physiology. Transgender Health. 4(1). 326–330. 13 indexed citations

Kensler, Kevin H., Meredith M. Regan, Yujing J. Heng, et al.. (2019). Prognostic and predictive value of androgen receptor expression in postmenopausal women with estrogen receptor-positive breast cancer: results from the Breast International Group Trial 1–98. Breast Cancer Research. 21(1). 30–30. 90 indexed citations

Baker, Gabrielle M., Michael E. Pyle, Josien P. W. Pluim, et al.. (2019). Detection of acini in histopathology slides: towards automated prediction of breast cancer risk. TU/e Research Portal. 3. 24–24. 2 indexed citations

10.

Kensler, Kevin H., Venkat Sankar, Jun Wang, et al.. (2018). PAM50 Molecular Intrinsic Subtypes in the Nurses' Health Study Cohorts. Cancer Epidemiology Biomarkers & Prevention. 28(4). 798–806. 37 indexed citations

11.

Hackney, Amy, et al.. (2011). The relationship between psychopathic traits and attachment behavior in a non-clinical population. Personality and Individual Differences. 51(5). 584–588. 38 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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