Kaveri A. Thakoor

512 total citations
30 papers, 326 citations indexed

About

Kaveri A. Thakoor is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Artificial Intelligence. According to data from OpenAlex, Kaveri A. Thakoor has authored 30 papers receiving a total of 326 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Ophthalmology and 7 papers in Artificial Intelligence. Recurrent topics in Kaveri A. Thakoor's work include Retinal Imaging and Analysis (21 papers), Glaucoma and retinal disorders (12 papers) and AI in cancer detection (5 papers). Kaveri A. Thakoor is often cited by papers focused on Retinal Imaging and Analysis (21 papers), Glaucoma and retinal disorders (12 papers) and AI in cancer detection (5 papers). Kaveri A. Thakoor collaborates with scholars based in United States, Israel and Australia. Kaveri A. Thakoor's co-authors include Paul Sajda, Donald C. Hood, Emmanouil Tsamis, Sharath Koorathota, Xinhui Li, Egill Hauksson, Thomas H. Heaton, Jennifer Andrews, Jeffrey M. Liebmann and George A. Cioffi and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Biomedical Engineering and Frontiers in Psychology.

In The Last Decade

Kaveri A. Thakoor

25 papers receiving 316 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kaveri A. Thakoor United States 8 247 217 68 56 52 30 326
Guangzhou An Japan 7 324 1.3× 251 1.2× 37 0.5× 50 0.9× 94 1.8× 10 387
Jaemin Son South Korea 7 352 1.4× 234 1.1× 68 1.0× 23 0.4× 136 2.6× 7 400
Hanpei Miao China 9 164 0.7× 124 0.6× 37 0.5× 29 0.5× 42 0.8× 21 253
Doug M. Baughman United States 2 402 1.6× 310 1.4× 37 0.5× 86 1.5× 81 1.6× 4 444
Sophie Klimscha Austria 5 345 1.4× 312 1.4× 59 0.9× 44 0.8× 29 0.6× 9 422
Diping Song China 6 186 0.8× 165 0.8× 36 0.5× 17 0.3× 63 1.2× 8 240
Mohammad Saleh Miri United States 8 331 1.3× 261 1.2× 14 0.2× 59 1.1× 213 4.1× 13 381
Parham Khojasteh Australia 7 212 0.9× 134 0.6× 35 0.5× 14 0.3× 104 2.0× 8 292
Arunava Chakravarty India 13 436 1.8× 330 1.5× 97 1.4× 52 0.9× 253 4.9× 18 531

Countries citing papers authored by Kaveri A. Thakoor

Since Specialization
Citations

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

Fields of papers citing papers by Kaveri A. Thakoor

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kaveri A. Thakoor. 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 Kaveri A. Thakoor. The network helps show where Kaveri A. Thakoor may publish in the future.

Co-authorship network of co-authors of Kaveri A. Thakoor

This figure shows the co-authorship network connecting the top 25 collaborators of Kaveri A. Thakoor. A scholar is included among the top collaborators of Kaveri A. Thakoor 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 Kaveri A. Thakoor. Kaveri A. Thakoor 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.
2.
White, Richard, et al.. (2025). Chest X-Ray Visual Saliency Modeling: Eye-Tracking Dataset and Saliency Prediction Model. IEEE Transactions on Neural Networks and Learning Systems. 36(9). 16920–16930. 2 indexed citations
3.
McCarthy, Angela, et al.. (2025). A Practical Guide to Evaluating Artificial Intelligence Imaging Models in Scientific Literature. Ophthalmology Science. 5(6). 100847–100847.
4.
Leshno, Ari, et al.. (2025). A Deep Learning Model Detects Glaucoma Based on an OCT Report, but Where Should the Clinician Look to Identify Glaucomatous Damage?. Translational Vision Science & Technology. 14(10). 23–23.
5.
Thakoor, Kaveri A., et al.. (2025). Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings. Translational Vision Science & Technology. 14(1). 20–20. 2 indexed citations
6.
Laine, Andrew F., et al.. (2024). Predicting Clinician Fixations on Glaucoma OCT Reports via CNN-Based Saliency Prediction Methods. IEEE Open Journal of Engineering in Medicine and Biology. 5. 191–197.
7.
Sharma, Anurag, et al.. (2024). Automated Identification of Clinically Relevant Regions in Glaucoma OCT Reports Using Expert Eye Tracking Data and Deep Learning. Translational Vision Science & Technology. 13(10). 24–24. 2 indexed citations
8.
Liebmann, Jeffrey M., et al.. (2023). Extracting decision-making features from the unstructured eye movements of clinicians on glaucoma OCT reports and developing AI models to classify expertise. Frontiers in Medicine. 10. 1251183–1251183. 5 indexed citations
9.
Sun, Yifan, et al.. (2023). Detecting Eye Disease Using Vision Transformers Informed by Ophthalmology Resident Gaze Data *. PubMed. 2023. 1–4. 5 indexed citations
10.
Hood, Donald C., Emmanouil Tsamis, Kaveri A. Thakoor, et al.. (2022). Detecting glaucoma with only OCT: Implications for the clinic, research, screening, and AI development. Progress in Retinal and Eye Research. 90. 101052–101052. 64 indexed citations
11.
Thakoor, Kaveri A., et al.. (2022). A multimodal deep learning system to distinguish late stages of AMD and to compare expert vs. AI ocular biomarkers. Scientific Reports. 12(1). 2585–2585. 33 indexed citations
12.
Thakoor, Kaveri A., et al.. (2021). A Hybrid Deep Learning System to Distinguish Late Stages of AMD and to Compare Expert vs. Machine AMD Risk Features. Investigative Ophthalmology & Visual Science. 62(8). 2146–2146. 1 indexed citations
13.
Thakoor, Kaveri A., Xinhui Li, Emmanouil Tsamis, et al.. (2021). Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans. Translational Vision Science & Technology. 10(4). 16–16. 14 indexed citations
14.
Koorathota, Sharath, et al.. (2021). A Recurrent Neural Network for Attenuating Non-cognitive Components of Pupil Dynamics. Frontiers in Psychology. 12. 604522–604522. 2 indexed citations
15.
Thakoor, Kaveri A., Emmanouil Tsamis, Carlos Gustavo De Moraes, Paul Sajda, & Donald C. Hood. (2020). Impact of Reference Standard, Data Augmentation, and OCT Input on Glaucoma Detection Accuracy by CNNs on a New Test Set. Investigative Ophthalmology & Visual Science. 61(7). 4540–4540. 2 indexed citations
16.
Li, Xinhui, et al.. (2020). Evaluating the transferability of deep learning models that distinguish glaucomatous from non-glaucomatous OCT circumpapillary disc scans. Investigative Ophthalmology & Visual Science. 61(7). 4548–4548. 1 indexed citations
17.
Koorathota, Sharath, et al.. (2020). Sequence Models in Eye Tracking: Predicting Pupil Diameter During Learning. 1–3. 3 indexed citations
18.
Thakoor, Kaveri A., Qian Zheng, Linyong Nan, et al.. (2019). Assessing the Ability of Convolutional Neural Networks to Detect Glaucoma from OCT Probability Maps. Investigative Ophthalmology & Visual Science. 60(9). 1464–1464. 1 indexed citations
19.
Thakoor, Kaveri A., Xinhui Li, Emmanouil Tsamis, Paul Sajda, & Donald C. Hood. (2019). Enhancing the Accuracy of Glaucoma Detection from OCT Probability Maps using Convolutional Neural Networks. PubMed. 2019. 2036–2040. 54 indexed citations
20.

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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