Mikhail Teverovskiy

751 total citations
13 papers, 459 citations indexed

About

Mikhail Teverovskiy is a scholar working on Artificial Intelligence, Molecular Biology and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Mikhail Teverovskiy has authored 13 papers receiving a total of 459 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Molecular Biology and 4 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Mikhail Teverovskiy's work include AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Spectroscopy Techniques in Biomedical and Chemical Research (3 papers). Mikhail Teverovskiy is often cited by papers focused on AI in cancer detection (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers) and Spectroscopy Techniques in Biomedical and Chemical Research (3 papers). Mikhail Teverovskiy collaborates with scholars based in United States and United Kingdom. Mikhail Teverovskiy's co-authors include Ali Tabesh, David Verbel, Ho-Yuen Pang, Angeliki Kotsianti, Olivier Saidi, Michael Donovan, Faisal M. Khan, Ricardo Mesa‐Tejada, Stefan Hamann and Carlos Cordon‐Cardo and has published in prestigious journals such as Journal of Clinical Oncology, The Journal of Urology and IEEE Transactions on Medical Imaging.

In The Last Decade

Mikhail Teverovskiy

13 papers receiving 439 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mikhail Teverovskiy United States 8 318 195 109 105 77 13 459
Olivier Saidi United States 5 286 0.9× 179 0.9× 95 0.9× 58 0.6× 63 0.8× 10 383
Deborah Thompson United States 11 233 0.7× 131 0.7× 109 1.0× 66 0.6× 60 0.8× 30 469
Ho-Yuen Pang United States 6 282 0.9× 189 1.0× 93 0.9× 47 0.4× 70 0.9× 11 375
Sahirzeeshan Ali United States 10 259 0.8× 118 0.6× 183 1.7× 76 0.7× 57 0.7× 15 362
Xinliang Zhu United States 10 520 1.6× 198 1.0× 354 3.2× 49 0.5× 67 0.9× 24 715
John Tomaszeweski United States 10 349 1.1× 309 1.6× 239 2.2× 130 1.2× 92 1.2× 12 586
Pooya Mobadersany United States 5 432 1.4× 94 0.5× 420 3.9× 84 0.8× 87 1.1× 9 716
Nathan Ing United States 6 217 0.7× 73 0.4× 165 1.5× 103 1.0× 38 0.5× 11 411
Rob van de Loo Netherlands 5 297 0.9× 126 0.6× 212 1.9× 35 0.3× 71 0.9× 5 379
Zhaoxuan Ma United States 8 254 0.8× 84 0.4× 182 1.7× 86 0.8× 53 0.7× 9 377

Countries citing papers authored by Mikhail Teverovskiy

Since Specialization
Citations

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

Fields of papers citing papers by Mikhail Teverovskiy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mikhail Teverovskiy

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

All Works

13 of 13 papers shown
1.
2.
Donovan, Michael, Angeliki Kotsianti, David Verbel, et al.. (2009). A systems pathology model for predicting overall survival in patients with refractory, advanced non-small-cell lung cancer treated with gefitinib. European Journal of Cancer. 45(8). 1518–1526. 10 indexed citations
3.
Tabesh, Ali, Mikhail Teverovskiy, Faisal M. Khan, et al.. (2009). Robust tumor morphometry in multispectral fluorescence microscopy. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 7260. 726015–726015. 8 indexed citations
4.
Donovan, Michael, Stefan Hamann, Faisal M. Khan, et al.. (2008). Systems Pathology Approach for the Prediction of Prostate Cancer Progression After Radical Prostatectomy. Journal of Clinical Oncology. 26(24). 3923–3929. 70 indexed citations
5.
Teverovskiy, Mikhail, Ali Tabesh, Ho-Yuen Pang, et al.. (2008). Automated localization and quantification of protein multiplexes via multispectral fluorescence imaging. 300–303. 13 indexed citations
6.
Cordon‐Cardo, Carlos, M. J. Donovan, F.A. Khan, et al.. (2007). Androgen receptor (AR) level in the prostatectomy specimen predicts time to disease progression post androgen suppression therapy. Journal of Clinical Oncology. 25(18_suppl). 5065–5065. 1 indexed citations
7.
Tabesh, Ali, Mikhail Teverovskiy, Ho-Yuen Pang, et al.. (2007). Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images. IEEE Transactions on Medical Imaging. 26(10). 1366–1378. 283 indexed citations
8.
Tabesh, Ali & Mikhail Teverovskiy. (2006). Tumor Classification in Histological Images of Prostate Using Color Texture. 841–845. 15 indexed citations
9.
Cordon‐Cardo, Carlos, Angeliki Kotsianti, Michael J. Donovan, et al.. (2005). 409: A Systems Pathology Approach to Predict Survival after Radical Prostatectomy. The Journal of Urology. 173(4S). 112–112. 1 indexed citations
10.
Teverovskiy, Mikhail, Junshui Ma, Angeliki Kotsianti, et al.. (2005). Improved prediction of prostate cancer recurrence based on an automated tissue image analysis system. 2. 257–260. 20 indexed citations
11.
Tabesh, Ali, Ho-Yuen Pang, David Verbel, et al.. (2005). Automated prostate cancer diagnosis and Gleason grading of tissue microarrays. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5747. 58–58. 32 indexed citations
12.
Cordon‐Cardo, Carlos, Angeliki Kotsianti, Michael Donovan, et al.. (2004). Improved prediction of PSA recurrence through systems pathology. Journal of Clinical Oncology. 22(14_suppl). 4591–4591. 2 indexed citations
13.
Cordon‐Cardo, Carlos, Angeliki Kotsianti, Michael Donovan, et al.. (2004). Improved prediction of PSA recurrence through systems pathology. Journal of Clinical Oncology. 22(14_suppl). 4591–4591. 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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