Thomas E. Tavolara

534 total citations
36 papers, 336 citations indexed

About

Thomas E. Tavolara is a scholar working on Artificial Intelligence, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Thomas E. Tavolara has authored 36 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 16 papers in Oncology and 13 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Thomas E. Tavolara's work include AI in cancer detection (17 papers), Radiomics and Machine Learning in Medical Imaging (12 papers) and Colorectal Cancer Screening and Detection (8 papers). Thomas E. Tavolara is often cited by papers focused on AI in cancer detection (17 papers), Radiomics and Machine Learning in Medical Imaging (12 papers) and Colorectal Cancer Screening and Detection (8 papers). Thomas E. Tavolara collaborates with scholars based in United States, Canada and Singapore. Thomas E. Tavolara's co-authors include Metin N. Gürcan, Muhammad Khalid Khan Niazi, Scott Segal, Gillian Beamer, Liron Pantanowitz, Sang Jin Lee, Douglas J. Hartman, Adam C. Gower, Wendy L. Frankel and Daniel M. Gatti and has published in prestigious journals such as PLoS ONE, Scientific Reports and The Journal of Urology.

In The Last Decade

Thomas E. Tavolara

36 papers receiving 325 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas E. Tavolara United States 12 157 134 81 55 44 36 336
Rajendra S. Sonawane India 11 136 0.9× 112 0.8× 171 2.1× 72 1.3× 50 1.1× 20 539
Norbert Wey Switzerland 4 230 1.5× 156 1.2× 108 1.3× 69 1.3× 107 2.4× 6 389
Masoumeh Gity Iran 14 121 0.8× 279 2.1× 56 0.7× 91 1.7× 13 0.3× 67 511
Mishka Gidwani United States 5 143 0.9× 315 2.4× 65 0.8× 20 0.4× 22 0.5× 7 480
Bowen Xin China 11 50 0.3× 224 1.7× 76 0.9× 26 0.5× 18 0.4× 22 404
Nicholas Meti Canada 9 96 0.6× 91 0.7× 109 1.3× 28 0.5× 23 0.5× 21 289
Elham Vali Betts United States 3 90 0.6× 69 0.5× 38 0.5× 20 0.4× 18 0.4× 7 276
Yijie Dong China 16 142 0.9× 447 3.3× 42 0.5× 40 0.7× 45 1.0× 52 734
Ai Dozen Japan 10 134 0.9× 203 1.5× 24 0.3× 29 0.5× 72 1.6× 12 483
Yinhui Deng China 10 44 0.3× 174 1.3× 27 0.3× 44 0.8× 45 1.0× 19 457

Countries citing papers authored by Thomas E. Tavolara

Since Specialization
Citations

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

Fields of papers citing papers by Thomas E. Tavolara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas E. Tavolara

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas E. Tavolara. A scholar is included among the top collaborators of Thomas E. Tavolara 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 Thomas E. Tavolara. Thomas E. Tavolara 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.
Zarella, Mark D., et al.. (2025). Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning. The Journal of Pathology Clinical Research. 11(2). e70024–e70024. 2 indexed citations
2.
Chen, Wei, et al.. (2024). Enhancing colorectal cancer tumor bud detection using deep learning from routine H&E-stained slides. PubMed. 12933. 27–27. 2 indexed citations
3.
Tavolara, Thomas E., Muhammad Khalid Khan Niazi, Andrew L. Feldman, et al.. (2024). Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning. Diagnostic Pathology. 19(1). 17–17. 4 indexed citations
4.
Gatti, Daniel M., Anna L. Tyler, J. Matthew Mahoney, et al.. (2024). Systems genetics uncover new loci containing functional gene candidates in Mycobacterium tuberculosis-infected Diversity Outbred mice. PLoS Pathogens. 20(6). e1011915–e1011915. 4 indexed citations
5.
Tavolara, Thomas E., Muhammad Khalid Khan Niazi, David L. Jaye, et al.. (2023). Deep learning to predict the proportion of positive cells in CMYC-stained tissue microarrays of diffuse large B-cell lymphoma. 3–3. 1 indexed citations
6.
Tavolara, Thomas E., Wei Chen, Wendy L. Frankel, Metin N. Gürcan, & Muhammad Khalid Khan Niazi. (2023). Minimizing the intra-pathologist disagreement for tumor bud detection on H and E images using weakly supervised learning. 42–42. 2 indexed citations
7.
Niazi, Muhammad Khalid Khan, Thomas E. Tavolara, Shuo Niu, et al.. (2023). BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images. PLoS ONE. 18(4). e0283562–e0283562. 15 indexed citations
8.
Tavolara, Thomas E., Muhammad Khalid Khan Niazi, & Metin N. Gürcan. (2023). Background detection affects downstream classification of Camelyon16 whole slide images. 24–24. 2 indexed citations
9.
Tavolara, Thomas E., et al.. (2023). One label is all you need: Interpretable AI-enhanced histopathology for oncology. Seminars in Cancer Biology. 97. 70–85. 13 indexed citations
10.
Tavolara, Thomas E., et al.. (2023). Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models. PubMed. 12465. 98–98. 1 indexed citations
11.
Tavolara, Thomas E., Metin N. Gürcan, & Muhammad Khalid Khan Niazi. (2023). The effects of sparsity induction methods on attention-based multiple instance learning applied to Camelyon16. 22–22. 1 indexed citations
12.
Tavolara, Thomas E., et al.. (2022). Attention2majority: Weak multiple instance learning for regenerative kidney grading on whole slide images. Medical Image Analysis. 79. 102462–102462. 31 indexed citations
13.
Niazi, Muhammad Khalid Khan, Thomas E. Tavolara, Claudia Abeijón, et al.. (2021). CXCL1: A new diagnostic biomarker for human tuberculosis discovered using Diversity Outbred mice. PLoS Pathogens. 17(8). e1009773–e1009773. 27 indexed citations
14.
Tavolara, Thomas E., Metin N. Gürcan, Scott Segal, & Muhammad Khalid Khan Niazi. (2021). Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models. Computers in Biology and Medicine. 136. 104737–104737. 45 indexed citations
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17.
Gillispie, Gregory J., Thomas E. Tavolara, James J. Yoo, et al.. (2020). Automated Image Analysis Methodologies to Compute Bioink Printability. Advanced Engineering Materials. 23(4). 14 indexed citations
18.
Tavolara, Thomas E., Muhammad Khalid Khan Niazi, César Piedra-Mora, et al.. (2020). Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning. EBioMedicine. 62. 103094–103094. 20 indexed citations
19.
Tavolara, Thomas E., et al.. (2019). A modular cGAN classification framework: Application to colorectal tumor detection. Scientific Reports. 9(1). 18969–18969. 20 indexed citations
20.
Ding, Yifu, Thomas E. Tavolara, & Keith C. Cheng. (2016). Automated detection of retinal cell nuclei in 3D micro-CT images of zebrafish using support vector machine classification. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9791. 97911A–97911A. 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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