Darvin Yi

2.4k total citations
26 papers, 1.2k citations indexed

About

Darvin Yi is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Darvin Yi has authored 26 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Ophthalmology and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Darvin Yi's work include Retinal Imaging and Analysis (12 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Glaucoma and retinal disorders (4 papers). Darvin Yi is often cited by papers focused on Retinal Imaging and Analysis (12 papers), Radiomics and Machine Learning in Medical Imaging (4 papers) and Glaucoma and retinal disorders (4 papers). Darvin Yi collaborates with scholars based in United States, Norway and Italy. Darvin Yi's co-authors include Daniel L. Rubin, Carson Lam, Zeshan Hussain, Francisco Javier Giménez Fuentes‐Guerra, Tony Lindsey, Margaret Guo, Endre Grøvik, Elizabeth Tong, Michael Iv and Greg Zaharchuk and has published in prestigious journals such as SHILAP Revista de lepidopterología, Radiology and PLoS Biology.

In The Last Decade

Darvin Yi

20 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Darvin Yi United States 12 778 359 246 212 184 26 1.2k
Carson Lam United States 13 606 0.8× 275 0.8× 179 0.7× 290 1.4× 106 0.6× 25 1.1k
Ken Chang United States 23 1.4k 1.8× 521 1.5× 169 0.7× 225 1.1× 336 1.8× 51 2.2k
Jorge Novo Spain 22 1.1k 1.4× 293 0.8× 341 1.4× 605 2.9× 174 0.9× 142 1.7k
Georgios C. Manikis Greece 15 489 0.6× 136 0.4× 110 0.4× 132 0.6× 73 0.4× 65 795
Jai Prashanth Rao Singapore 8 555 0.7× 515 1.4× 356 1.4× 23 0.1× 96 0.5× 19 1.3k
Georgios Z. Papadakis United States 20 582 0.7× 252 0.7× 126 0.5× 43 0.2× 347 1.9× 83 1.6k
Yuchen Qiu United States 18 1.1k 1.4× 859 2.4× 331 1.3× 25 0.1× 271 1.5× 62 1.8k
Chung‐Ming Lo Taiwan 21 746 1.0× 564 1.6× 284 1.2× 21 0.1× 197 1.1× 62 1.2k
Qiangguo Jin China 10 703 0.9× 355 1.0× 663 2.7× 262 1.2× 55 0.3× 32 1.3k
Mohamed Shehata Egypt 14 377 0.5× 152 0.4× 122 0.5× 19 0.1× 142 0.8× 55 648

Countries citing papers authored by Darvin Yi

Since Specialization
Citations

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

Fields of papers citing papers by Darvin Yi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Darvin Yi

This figure shows the co-authorship network connecting the top 25 collaborators of Darvin Yi. A scholar is included among the top collaborators of Darvin Yi 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 Darvin Yi. Darvin Yi 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.
Nahass, George R., et al.. (2025). Open-Source Periorbital Segmentation Dataset for Ophthalmic Applications. Ophthalmology Science. 5(4). 100757–100757.
2.
Nahass, George R., D. K. Bradley, Chad A. Purnell, et al.. (2025). Development and Validation of a Semiautomated Tool for Measuring Periorbital Distances. Ophthalmology Science. 5(6). 100887–100887.
3.
Yi, Darvin, et al.. (2024). Automated segmentation for early detection of uveal melanoma. Canadian Journal of Ophthalmology. 59(6). e784–e791. 4 indexed citations
4.
Li, Yanliang, et al.. (2024). Resilience to diabetic retinopathy. Progress in Retinal and Eye Research. 101. 101271–101271. 20 indexed citations
5.
6.
Santambrogio, Marco D., et al.. (2024). Repurposing the Image Generative Potential: Exploiting GANs to Grade Diabetic Retinopathy. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 2305–2314. 2 indexed citations
7.
Leiderman, Yannek I., Matthew J. Gerber, Jean‐Pierre Hubschman, & Darvin Yi. (2024). Artificial intelligence applications in ophthalmic surgery. Current Opinion in Ophthalmology. 35(6). 526–532. 2 indexed citations
8.
Sethi, Abhishek, Jae Edmonds, R.V. Paul Chan, et al.. (2023). Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data. Translational Vision Science & Technology. 12(11). 8–8. 9 indexed citations
9.
Yi, Darvin, Elizabeth Tong, Michael Iv, et al.. (2023). 2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data. Frontiers in Neuroinformatics. 16. 1056068–1056068. 20 indexed citations
10.
Yi, Darvin, et al.. (2022). Feature Tracking and Segmentation in Real Time via Deep Learning in Vitreoretinal Surgery. Ophthalmology Retina. 7(3). 236–242. 12 indexed citations
11.
Cole, Emily, et al.. (2022). A Platform for Tracking Surgeon and Observer Gaze as a Surrogate for Attention in Ophthalmic Surgery. SHILAP Revista de lepidopterología. 3(2). 100246–100246. 2 indexed citations
12.
Yi, Darvin, et al.. (2022). Evaluation of Artificial Intelligence–Based Intraoperative Guidance Tools for Phacoemulsification Cataract Surgery. JAMA Ophthalmology. 140(2). 170–170. 29 indexed citations
13.
Grøvik, Endre, Darvin Yi, Michael Iv, et al.. (2021). Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study. npj Digital Medicine. 4(1). 33–33. 32 indexed citations
14.
Setabutr, Pete, et al.. (2021). AutoPtosis: A dual model system for rapid and automatic detection of ptosis. 62(8). 2160–2160. 1 indexed citations
15.
Rister, Blaine, Darvin Yi, Kaushik Shivakumar, Tomomi W. Nobashi, & Daniel L. Rubin. (2020). CT-ORG, a new dataset for multiple organ segmentation in computed tomography. Scientific Data. 7(1). 381–381. 83 indexed citations
16.
Grøvik, Endre, Darvin Yi, Michael Iv, et al.. (2019). Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. Journal of Magnetic Resonance Imaging. 51(1). 175–182. 170 indexed citations
17.
Dunnmon, Jared, Darvin Yi, Curtis P. Langlotz, et al.. (2018). Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs. Radiology. 290(2). 537–544. 150 indexed citations
18.
Huang, Laura C., Caroline Yu, Robert A. Kleinman, et al.. (2017). Opening the Black Box: Visualization of Deep Neural Network for Detection of Disease in Retinal Fundus Photographs. Investigative Ophthalmology & Visual Science. 58(8). 94–94.
19.
Yi, Darvin, et al.. (2017). A critical-like collective state leads to long-range cell communication in Dictyostelium discoideum aggregation. PLoS Biology. 15(4). e1002602–e1002602. 29 indexed citations
20.
Hussain, Zeshan, Francisco Javier Giménez Fuentes‐Guerra, Darvin Yi, & Daniel L. Rubin. (2017). Differential Data Augmentation Techniques for Medical Imaging Classification Tasks.. PubMed. 2017. 979–984. 215 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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