Philipp Seeböck

4.8k total citations · 1 hit paper
19 papers, 1.2k citations indexed

About

Philipp Seeböck is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Philipp Seeböck has authored 19 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 11 papers in Ophthalmology and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Philipp Seeböck's work include Retinal Imaging and Analysis (17 papers), Retinal Diseases and Treatments (9 papers) and Retinal and Optic Conditions (5 papers). Philipp Seeböck is often cited by papers focused on Retinal Imaging and Analysis (17 papers), Retinal Diseases and Treatments (9 papers) and Retinal and Optic Conditions (5 papers). Philipp Seeböck collaborates with scholars based in Austria, Argentina and United States. Philipp Seeböck's co-authors include Ursula Schmidt‐Erfurth, Sebastian M. Waldstein, Georg Langs, Thomas Schlegl, Hrvoje Bogunović, José Ignacio Orlando, Bianca S. Gerendas, Sophie Klimscha, Wolf‐Dieter Vogl and Amir Sadeghipour and has published in prestigious journals such as Scientific Reports, IEEE Transactions on Medical Imaging and Progress in Retinal and Eye Research.

In The Last Decade

Philipp Seeböck

19 papers receiving 1.1k citations

Hit Papers

f-AnoGAN: Fast unsupervised anomaly detection with genera... 2019 2026 2021 2023 2019 250 500 750
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. f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
    2019 845 citations Thomas Schlegl, Philipp Seeböck et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Philipp Seeböck Austria 9 661 460 274 217 160 19 1.2k
Evangelos Dermatas Greece 15 406 0.6× 264 0.6× 453 1.7× 155 0.7× 88 0.6× 71 991
Yanjun Peng China 21 343 0.5× 276 0.6× 538 2.0× 57 0.3× 50 0.3× 108 1.8k
P. Palanisamy India 23 269 0.4× 700 1.5× 636 2.3× 379 1.7× 23 0.1× 144 1.9k
Il Dong Yun South Korea 19 197 0.3× 255 0.6× 542 2.0× 81 0.4× 23 0.1× 77 1.0k
Barath Narayanan Narayanan United States 16 289 0.4× 235 0.5× 171 0.6× 20 0.1× 167 1.0× 33 746

Countries citing papers authored by Philipp Seeböck

Since Specialization
Citations

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

Fields of papers citing papers by Philipp Seeböck

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philipp Seeböck

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

All Works

19 of 19 papers shown
1.
Seeböck, Philipp, et al.. (2024). Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection. Medical Image Analysis. 93. 103104–103104. 8 indexed citations
2.
Reiter, Gregor S., Hrvoje Bogunović, Ferdinand Georg Schlanitz, et al.. (2023). Point-to-point associations of drusen and hyperreflective foci volumes with retinal sensitivity in non-exudative age-related macular degeneration. Eye. 37(17). 3582–3588. 10 indexed citations
3.
Bernathova, Maria, et al.. (2023). Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study. European Radiology Experimental. 7(1). 32–32. 6 indexed citations
4.
König, Michael, et al.. (2022). Quality assessment of colour fundus and fluorescein angiography images using deep learning. British Journal of Ophthalmology. 108(1). 98–104. 7 indexed citations
5.
Seeböck, Philipp, Wolf‐Dieter Vogl, Sebastian M. Waldstein, et al.. (2022). Linking Function and Structure with ReSensNet. Ophthalmology Retina. 6(6). 501–511. 14 indexed citations
6.
7.
Schwarzenbacher, Luca, Philipp Seeböck, Daniel Schartmüller, et al.. (2022). Automatic segmentation of intraocular lens, the retrolental space and Berger's space using deep learning. Acta Ophthalmologica. 100(8). e1611–e1616. 6 indexed citations
8.
Hofer, Dominik, et al.. (2022). Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures. Biomedical Optics Express. 13(5). 2566–2566. 1 indexed citations
9.
Schmidt‐Erfurth, Ursula, Gregor S. Reiter, Sophie Riedl, et al.. (2021). AI-based monitoring of retinal fluid in disease activity and under therapy. Progress in Retinal and Eye Research. 86. 100972–100972. 59 indexed citations
10.
Schlanitz, Ferdinand Georg, Hrvoje Bogunović, Wolf‐Dieter Vogl, et al.. (2020). The Impact of Drusen on Retinal Sensitivity in non-exudative Age-Related Macular Degeneration: A point-to-point Analysis. Investigative Ophthalmology & Visual Science. 61(7). 1822–1822. 1 indexed citations
11.
Bogunović, Hrvoje, John W. Seaman, Philippe Margaron, et al.. (2020). Detection of retinal fluids in OCT scans by an automated deep learning algorithm compared to human expert grading in the HAWK & HARRIER trials. Investigative Ophthalmology & Visual Science. 61(7). 5187–5187. 2 indexed citations
12.
Seeböck, Philipp, et al.. (2020). Automated quantification of macular fluid in retinal diseases and their response to anti-VEGF therapy. British Journal of Ophthalmology. 106(1). 113–120. 35 indexed citations
13.
Waldstein, Sebastian M., Philipp Seeböck, René Donner, et al.. (2020). Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning. Scientific Reports. 10(1). 12954–12954. 21 indexed citations
14.
Seeböck, Philipp, David Romo‐Bucheli, Sebastian M. Waldstein, et al.. (2019). Using Cyclegans for Effectively Reducing Image Variability Across OCT Devices and Improving Retinal Fluid Segmentation. arXiv (Cornell University). 605–609. 5 indexed citations
15.
Seeböck, Philipp, Wolf‐Dieter Vogl, Sebastian M. Waldstein, et al.. (2019). Linking Function and Structure: Prediction of Retinal Sensitivity in AMD from OCT using Deep Learning. Investigative Ophthalmology & Visual Science. 60(9). 1534–1534. 1 indexed citations
16.
Romo‐Bucheli, David, Philipp Seeböck, José Ignacio Orlando, et al.. (2019). Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina. Biomedical Optics Express. 11(1). 346–346. 41 indexed citations
17.
Schlegl, Thomas, Philipp Seeböck, Sebastian M. Waldstein, Georg Langs, & Ursula Schmidt‐Erfurth. (2019). f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis. 54. 30–44. 845 indexed citations breakdown →
18.
Seeböck, Philipp, José Ignacio Orlando, Thomas Schlegl, et al.. (2019). Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT. IEEE Transactions on Medical Imaging. 39(1). 87–98. 100 indexed citations
19.
Seeböck, Philipp, Sebastian M. Waldstein, René Donner, et al.. (2017). Defining disease endophenotypes in neovascular AMD by unsupervised machine learning of large-scale OCT data. Investigative Ophthalmology & Visual Science. 58(8). 56–56. 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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