Peter Pinto

488 total citations
18 papers, 192 citations indexed

About

Peter Pinto is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence. According to data from OpenAlex, Peter Pinto has authored 18 papers receiving a total of 192 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Pulmonary and Respiratory Medicine, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Artificial Intelligence. Recurrent topics in Peter Pinto's work include Prostate Cancer Diagnosis and Treatment (12 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and AI in cancer detection (5 papers). Peter Pinto is often cited by papers focused on Prostate Cancer Diagnosis and Treatment (12 papers), Radiomics and Machine Learning in Medical Imaging (5 papers) and AI in cancer detection (5 papers). Peter Pinto collaborates with scholars based in United States, Canada and Singapore. Peter Pinto's co-authors include Peter L. Choyke, Bradford J. Wood, Sheng Xu, Barış Türkbey, Reza Seifabadi, Yue Chen, Allison Squires, Zion Tsz Ho Tse, Purang Abolmaesumi and Amir Tahmasebi and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Biomedical Engineering and European Urology.

In The Last Decade

Peter Pinto

17 papers receiving 187 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Peter Pinto United States 7 86 71 57 50 43 18 192
Bogdan Maris Italy 9 81 0.9× 83 1.2× 53 0.9× 40 0.8× 37 0.9× 29 270
Shinya Onogi Japan 10 190 2.2× 87 1.2× 26 0.5× 93 1.9× 15 0.3× 55 301
Mehran Pesteie Canada 10 79 0.9× 108 1.5× 30 0.5× 45 0.9× 67 1.6× 15 251
Håkon Olav Leira Norway 10 87 1.0× 102 1.4× 202 3.5× 103 2.1× 14 0.3× 37 368
Golnoosh Samei Switzerland 8 87 1.0× 92 1.3× 70 1.2× 41 0.8× 39 0.9× 17 227
Amir H. Abdi Canada 10 96 1.1× 167 2.4× 43 0.8× 21 0.4× 67 1.6× 23 392
Jianxin Guo China 12 182 2.1× 215 3.0× 38 0.7× 23 0.5× 16 0.4× 40 362
Ziyang Dong China 9 240 2.8× 67 0.9× 41 0.7× 75 1.5× 13 0.3× 15 296
Vincent Groenhuis Netherlands 12 282 3.3× 112 1.6× 51 0.9× 90 1.8× 31 0.7× 33 387

Countries citing papers authored by Peter Pinto

Since Specialization
Citations

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

Fields of papers citing papers by Peter Pinto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter Pinto

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

All Works

18 of 18 papers shown
1.
Pinto, Cathy Anne, Julie Fox, Ananya Roy, et al.. (2025). Longitudinal Evaluation of Clear-cell Renal Cell Carcinoma in von Hippel-Lindau Disease. European Urology. 88(1). 56–63. 1 indexed citations
2.
Zhang, Haoyue, Sushant Patkar, Rosina T. Lis, et al.. (2024). Masked Image Modeling Meets Self-Distillation: A Transformer-Based Prostate Gland Segmentation Framework for Pathology Slides. Cancers. 16(23). 3897–3897. 1 indexed citations
3.
4.
Kenigsberg, Alexander P., Yan Mee Law, Enis C. Yılmaz, et al.. (2024). Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System. European Urology Open Science. 62. 74–80. 12 indexed citations
5.
Harmon, Stephanie A., Alexander P. Kenigsberg, Yan Mee Law, et al.. (2024). Evaluating a deep learning AI algorithm for detecting residual prostate cancer on MRI after focal therapy. SHILAP Revista de lepidopterología. 5(7). 779–781. 1 indexed citations
6.
Yılmaz, Enis C., Stephanie A. Harmon, Rosina T. Lis, et al.. (2024). Evaluating deep learning and radiologist performance in volumetric prostate cancer analysis with biparametric MRI and histopathologically mapped slides. Abdominal Radiology. 50(6). 2732–2744.
7.
Mena, Esther, Liza Lindenberg, Nathan Lay, et al.. (2024). Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN. Oncotarget. 15(1). 288–300. 2 indexed citations
8.
Ozyoruk, Kutsev Bengisu, Stephanie A. Harmon, Nathan Lay, et al.. (2024). AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection. Journal of Personalized Medicine. 14(10). 1047–1047. 2 indexed citations
9.
Mena, Esther, Joanna H. Shih, Liza Lindenberg, et al.. (2023). Predicting 18F-DCFPyL-PET/CT Scan Positivity in Prostate Cancer Patients with Biochemical Recurrence. Academic Radiology. 31(4). 1419–1428. 1 indexed citations
10.
Azizi, Shekoofeh, Ming Li, Sheng Xu, et al.. (2018). Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy. International Journal of Computer Assisted Radiology and Surgery. 13(8). 1201–1209. 9 indexed citations
11.
Azizi, Shekoofeh, Parvin Mousavi, Pingkun Yan, et al.. (2017). Transfer learning from RF to B-mode temporal enhanced ultrasound features for prostate cancer detection. International Journal of Computer Assisted Radiology and Surgery. 12(7). 1111–1121. 23 indexed citations
12.
Chen, Yue, Sheng Xu, Allison Squires, et al.. (2017). MRI-Guided Robotically Assisted Focal Laser Ablation of the Prostate Using Canine Cadavers. IEEE Transactions on Biomedical Engineering. 65(7). 1434–1442. 35 indexed citations
13.
Chen, Yue, Allison Squires, Reza Seifabadi, et al.. (2016). Robotic System for MRI-Guided Focal Laser Ablation in the Prostate. IEEE/ASME Transactions on Mechatronics. 22(1). 107–114. 47 indexed citations
14.
Xu, Sheng, Harsh Agarwal, Marcelino Bernardo, et al.. (2016). An MRI guided system for prostate laser ablation with treatment planning and multi-planar temperature monitoring. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9786. 97861I–97861I. 2 indexed citations
15.
Azizi, Shekoofeh, Farhad Imani, Amir Tahmasebi, et al.. (2016). Detection of prostate cancer using temporal sequences of ultrasound data: a large clinical feasibility study. International Journal of Computer Assisted Radiology and Surgery. 11(6). 947–956. 34 indexed citations
16.
Amalou, Hayet, Anthony Hoang, Sheng Xu, et al.. (2013). Prostate cancer laser ablation with MRI or ultrasound guidance. Journal of Vascular and Interventional Radiology. 24(4). S99–S99. 1 indexed citations
17.
Wang, Shijun, Peter Liu, Barış Türkbey, et al.. (2012). Gaussian Process Inference for Estimating Pharmacokinetic Parameters of Dynamic Contrast-Enhanced MR Images. Lecture notes in computer science. 15(Pt 3). 582–589. 5 indexed citations
18.
Ng, Sean & Peter Pinto. (2000). Ultrasound-guided retrieval of labial minor salivary gland sialoliths. Dentomaxillofacial Radiology. 29(5). 319–322. 11 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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