Rory Sayres

5.1k total citations · 1 hit paper
29 papers, 1.5k citations indexed

About

Rory Sayres is a scholar working on Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Ophthalmology. According to data from OpenAlex, Rory Sayres has authored 29 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Cognitive Neuroscience, 10 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Ophthalmology. Recurrent topics in Rory Sayres's work include Retinal Imaging and Analysis (10 papers), Face Recognition and Perception (8 papers) and Retinal Diseases and Treatments (7 papers). Rory Sayres is often cited by papers focused on Retinal Imaging and Analysis (10 papers), Face Recognition and Perception (8 papers) and Retinal Diseases and Treatments (7 papers). Rory Sayres collaborates with scholars based in United States, India and Canada. Rory Sayres's co-authors include Kalanit Grill‐Spector, David Ress, BA Wandell, Hiroshi Horiguchi, Jonathan Winawer, Kaoru Amano, Dale R. Webster, Kevin S. Weiner, Greg S. Corrado and Lily Peng and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nature Neuroscience and Journal of Neurophysiology.

In The Last Decade

Rory Sayres

28 papers receiving 1.4k citations

Hit Papers

Using a Deep Learning Algorithm and Integrated Gradients ... 2018 2026 2020 2023 2018 50 100 150 200 250
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy
    2018 250 citations Rory Sayres, Ankur Taly et al. Ophthalmology profile →

Peers

Rory Sayres
Tobias Elze United States
Wolfgang Fuhl Germany
Noah Lee United States
Piers D. L. Howe Australia
Hatim Aboalsamh Saudi Arabia
G. Anthony Reina United States
Sampath Jayarathna United States
Mikhail Milchenko United States
Arantxa Villanueva Spain
Nicha C. Dvornek United States
Tobias Elze United States
Rory Sayres
Citations per year, relative to Rory Sayres Rory Sayres (= 1×) peers Tobias Elze

Countries citing papers authored by Rory Sayres

Since Specialization
Citations

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

Fields of papers citing papers by Rory Sayres

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rory Sayres

This figure shows the co-authorship network connecting the top 25 collaborators of Rory Sayres. A scholar is included among the top collaborators of Rory Sayres 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 Rory Sayres. Rory Sayres 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.
Devon-Sand, Anna, Rory Sayres, Yun Liu, et al.. (2024). A Multiparty Collaboration to Engage Diverse Populations in Community-Centered Artificial Intelligence Research. SHILAP Revista de lepidopterología. 2(3). 463–469.
3.
Chia, Mark A., Fred Hersch, Rory Sayres, et al.. (2023). Validation of a deep learning system for the detection of diabetic retinopathy in Indigenous Australians. British Journal of Ophthalmology. 108(2). 268–273. 13 indexed citations
4.
5.
Hammel, Naama, et al.. (2021). Iterative Quality Control Strategies for Expert Medical Image Labeling. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 9. 60–71. 11 indexed citations
6.
Sayres, Rory, et al.. (2020). Challenges in evaluating clinical deployments of Deep Learning Assisted Diagnostics for Diabetic Retinopathy Screening. Investigative Ophthalmology & Visual Science. 61(7). 2045–2045. 2 indexed citations
7.
Soonthornworasiri, Ngamphol, Chetan Rao, Rajiv Raman, et al.. (2020). Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders. Journal of Diabetes Research. 2020. 1–8. 12 indexed citations
8.
Sundararajan, Mukund, et al.. (2019). Exploring Principled Visualizations for Deep Network Attributions.. 10 indexed citations
9.
Hammel, Naama, Mike Schaekermann, Sonia Phene, et al.. (2019). A Study of Feature-based Consensus Formation for Glaucoma Risk Assessment. Investigative Ophthalmology & Visual Science. 60(9). 164–164. 1 indexed citations
10.
Schaekermann, Mike, Naama Hammel, Bilson Campana, et al.. (2019). Asynchronous Remote Adjudication for Grading Diabetic Retinopathy. Investigative Ophthalmology & Visual Science. 60(9). 158–158. 1 indexed citations
11.
Phene, Sonia, Naama Hammel, Yun Liu, et al.. (2019). Deep Learning and Glaucoma Specialists. Ophthalmology. 126(12). 1627–1639. 126 indexed citations
12.
Raghu, Maithra, Katy Blumer, Rory Sayres, et al.. (2018). Direct Uncertainty Prediction with Applications to Healthcare.. arXiv (Cornell University). 3 indexed citations
13.
Sayres, Rory, Ankur Taly, Ehsan Rahimy, et al.. (2018). Assisted reads for diabetic retinopathy using a deep learning algorithm and integrated gradient explanation. Investigative Ophthalmology & Visual Science. 59(9). 1227–1227. 2 indexed citations
14.
Sayres, Rory, Ankur Taly, Ehsan Rahimy, et al.. (2018). Using a Deep Learning Algorithm and Integrated Gradients Explanation to Assist Grading for Diabetic Retinopathy. Ophthalmology. 126(4). 552–564. 250 indexed citations breakdown →
15.
Raghu, Maithra, Katy Blumer, Rory Sayres, et al.. (2018). Direct Uncertainty Prediction for Medical Second Opinions. arXiv (Cornell University). 5281–5290. 23 indexed citations
16.
Navalpakkam, Vidhya, et al.. (2013). Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts. 953–964. 76 indexed citations
17.
Winawer, Jonathan, Hiroshi Horiguchi, Rory Sayres, Kaoru Amano, & BA Wandell. (2010). Mapping hV4 and ventral occipital cortex: The venous eclipse. Journal of Vision. 10(5). 1–1. 221 indexed citations
18.
Sayres, Rory & Kalanit Grill‐Spector. (2008). Relating Retinotopic and Object-Selective Responses in Human Lateral Occipital Cortex. Journal of Neurophysiology. 100(1). 249–267. 136 indexed citations
19.
Sayres, Rory, David Ress, & Kalanit Grill‐Spector. (2005). Identifying Distributed Object Representations in Human Extrastriate Visual Cortex. Neural Information Processing Systems. 18. 1169–1176. 3 indexed citations
20.
Sayres, Rory & Kalanit Grill‐Spector. (2005). Object-Selective Cortex Exhibits Performance-Independent Repetition Suppression. Journal of Neurophysiology. 95(2). 995–1007. 114 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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