Julia Mai

525 total citations
27 papers, 346 citations indexed

About

Julia Mai is a scholar working on Radiology, Nuclear Medicine and Imaging, Ophthalmology and Biomedical Engineering. According to data from OpenAlex, Julia Mai has authored 27 papers receiving a total of 346 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Radiology, Nuclear Medicine and Imaging, 20 papers in Ophthalmology and 6 papers in Biomedical Engineering. Recurrent topics in Julia Mai's work include Retinal Imaging and Analysis (21 papers), Retinal Diseases and Treatments (18 papers) and Retinal and Optic Conditions (7 papers). Julia Mai is often cited by papers focused on Retinal Imaging and Analysis (21 papers), Retinal Diseases and Treatments (18 papers) and Retinal and Optic Conditions (7 papers). Julia Mai collaborates with scholars based in Austria, Switzerland and United Kingdom. Julia Mai's co-authors include Hrvoje Bogunović, Ursula Schmidt‐Erfurth, Sophie Riedl, Gregor S. Reiter, Dmitrii Lachinov, Wolf‐Dieter Vogl, Alex McKeown, Christoph Grechenig, Ulf Teichgräber and Tobias Franiel and has published in prestigious journals such as Scientific Reports, Ophthalmology and IEEE Access.

In The Last Decade

Julia Mai

24 papers receiving 345 citations

Peers

Julia Mai

RNMI

OPHTH

PRM

Dmitrii Lachinov Austria

Duriye Damla Sevgi United States

Sophie Riedl Austria

Magdalena Baratsits Austria

Ermete Giancipoli Italy

Paola Marolo Italy

Michael Dubow United States

Anna Sala‐Puigdollers Spain

Ilana Traynis United States

Mingzhen Yuan China

Dmitrii Lachinov Austria

Julia Mai

186 ×0.7

RNMI

204 ×0.8

OPHTH

14 ×0.3

PRM

38 ×0.9

21 ×0.8

Citations per year, relative to Julia Mai Julia Mai (= 1×) peers Dmitrii Lachinov

Countries citing papers authored by Julia Mai

Since Specialization

Citations

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

Fields of papers citing papers by Julia Mai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Julia Mai

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

All Works

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20 of 20 papers shown

Aresta, Guilherme, Dmitrii Lachinov, Sophie Riedl, et al.. (2025). SD-LayerNet: Robust and label-efficient retinal layer segmentation via anatomical priors. Computer Methods and Programs in Biomedicine. 261. 108586–108586. 2 indexed citations

Reiter, Gregor S., et al.. (2024). AI in the clinical management of GA: A novel therapeutic universe requires novel tools. Progress in Retinal and Eye Research. 103. 101305–101305. 7 indexed citations

Riedl, Sophie, Ursula Schmidt‐Erfurth, Julia Mai, et al.. (2024). Sequence of Morphological Changes Preceding Atrophy in Intermediate AMD Using Deep Learning. Investigative Ophthalmology & Visual Science. 65(8). 30–30. 7 indexed citations

Schmidt‐Erfurth, Ursula, Julia Mai, Gregor S. Reiter, et al.. (2024). Disease Activity and Therapeutic Response to Pegcetacoplan for Geographic Atrophy Identified by Deep Learning-Based Analysis of OCT. Ophthalmology. 132(2). 181–193. 14 indexed citations

Aresta, Guilherme, et al.. (2024). Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps. IEEE Access. 12. 47009–47023. 2 indexed citations

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

Mai, Julia, Dmitrii Lachinov, Gregor S. Reiter, et al.. (2024). Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCT. Ophthalmology Science. 4(4). 100466–100466. 10 indexed citations

Reiter, Gregor S., Dmitrii Lachinov, Günther Weigert, et al.. (2024). A Novel Management Challenge in Age-Related Macular Degeneration. Ophthalmology Retina. 9(5). 421–430. 2 indexed citations

Mai, Julia & Ursula Schmidt‐Erfurth. (2024). Role of Artificial Intelligence in Retinal Diseases. Klinische Monatsblätter für Augenheilkunde. 241(9). 1023–1031.

10.

Steiner, Irene, Philipp Fuchs, Julia Mai, et al.. (2024). Real-World Outcomes After Switch From Aflibercept to Faricimab in Eyes With Diabetic Macular Edema. Investigative Ophthalmology & Visual Science. 65(14). 46–46. 3 indexed citations

11.

Traber, Ghislaine L., Maximilian Pfau, Sophie Riedl, et al.. (2023). Comparison of Novel Volumetric Microperimetry Metrics in Intermediate Age-Related Macular Degeneration: PINNACLE Study Report 3. Translational Vision Science & Technology. 12(8). 21–21. 2 indexed citations

12.

Schmidt‐Erfurth, Ursula, Julia Mai, Gregor S. Reiter, et al.. (2023). Monitoring der Progression von geografischer Atrophie in der optischen Kohärenztomographie. Die Ophthalmologie. 120(9). 965–969. 1 indexed citations

13.

Riedl, Sophie, Julia Mai, Gregor S. Reiter, et al.. (2023). Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5). Scientific Reports. 13(1). 19545–19545. 18 indexed citations

14.

Mai, Julia, Dmitrii Lachinov, Sophie Riedl, et al.. (2023). Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment. Scientific Reports. 13(1). 7028–7028. 22 indexed citations

15.

Mai, Julia, Sophie Riedl, Gregor S. Reiter, et al.. (2022). Comparison of Fundus Autofluorescence Versus Optical Coherence Tomography–based Evaluation of the Therapeutic Response to Pegcetacoplan in Geographic Atrophy. American Journal of Ophthalmology. 244. 175–182. 28 indexed citations

16.

Vogl, Wolf‐Dieter, Sophie Riedl, Julia Mai, et al.. (2022). Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning. Ophthalmology Retina. 7(1). 4–13. 50 indexed citations

17.

Riedl, Sophie, Wolf‐Dieter Vogl, Julia Mai, et al.. (2022). The Effect of Pegcetacoplan Treatment on Photoreceptor Maintenance in Geographic Atrophy Monitored by Artificial Intelligence–Based OCT Analysis. Ophthalmology Retina. 6(11). 1009–1018. 71 indexed citations

18.

Lachinov, Dmitrii, et al.. (2022). Segmentation of Bruch's Membrane in Retinal OCT With AMD Using Anatomical Priors and Uncertainty Quantification. IEEE Journal of Biomedical and Health Informatics. 27(1). 41–52. 19 indexed citations

19.

Reiter, Gregor S., Sophie Riedl, Julia Mai, et al.. (2021). Identification of initial events leading to outer retinal atrophy in age-related macular degeneration using deep learning quantifications. Investigative Ophthalmology & Visual Science. 62(8). 121–121. 2 indexed citations

20.

Mai, Julia, Thomas Lehmann, Tom Hilbert, et al.. (2018). T2 Mapping in Prostate Cancer. Investigative Radiology. 54(3). 146–152. 59 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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