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Christian Doppler Laboratory for Artificial Intelligence in Retina

CD AIR

Christian Doppler Laboratory for Artificial Intelligence in Retina – “CD AIR”

The Lab is focused on solving socio-technical hurdles for the deployment of AI in eye care as clinical-decision support systems. This interdisciplinary research team is composed of retinal specialists, computer scientists and software engineers, developing innovative image analysis methods for precision medicine in retinal disease.

The overarching goal of the Laboratory is to enable AI-driven clinical decision support systems (CDSSs) for the effective management of retinal diseases, representing the leading causes of blindness. The CDSS would naturally be rooted in rich multi-modal imaging offered by OCT device manufacturers, but would also exploit the data available in the associated EHR. Such modern AI based CDSS in retina is expected to:

  • automate tedious tasks and provide objective examination summaries,
  • allow detecting pathological changes of retina early and precisely, and
  • improve the clinical workflow efficiency allowing retinal specialists to examine more patients more accurately. This would constitute a groundbreaking raise of the level of healthcare quality in ophthalmology.

The research will advance our understanding of the most devastating blinding diseases. It will widely expand the spectrum of accessible biomarkers in the current diagnostic and therapeutic area going far beyond the previously known features and established paradigms, open the horizon for novel subclinical biomarker detection providing a novel understanding of disease mechanisms and identification of novel therapeutic targets.


Challenges ahead

With the enormous advance of AI over the past decade, nowadays capable of a human-level pattern recognition performance, uptake of deep learning models in clinical routine has nevertheless still not happened. This stems from following very unique challenges that machine learning technology is facing when being deployed in a medical setting.

  • High-dimensional Data
    Medical data is dense rather than big, the so called “large p, small n” problem. There is often a vast amount of multi-modal detail available about a patient while the number of available patients is limited. Such a setting poses a considerable challenge for an effective machine learning.
  • Data quality
    Data is the fuel that powers AI, though much of real-world medical data is heterogeneous, noisy and incomplete or missing, acquired with different imaging devices and protocols. There is a large variability of expert opinions and ground truth labels are often not retrievable. Thus, machine learning methods have to handle data sparsity, and missing or incorrect values and labels.
  • The last mile problem, aka the final percent
    In a safety-critical environment like medicine, any wrong conclusion could have a catastrophic downstream effect. Thus, it is important that AI demonstrate robustness, and is able to handle also rare, corner-case situations, which is often ignored in performance evaluations. Currently, a clinician in the loop is needed to assure the credibility of the output.
  • The distribution shift
    It poses a fundamental limitation to machine learning abilities. As the imaging equipment changes, the models trained on retrospective data may not generalize to the newly acquired data anymore. Thus, algorithms that can withstand some extent of distribution shift are needed.
  • Supervision
    Most of current deep learning is supervised with a significant cost and manual effort required to annotate often at the pixel-level the large amount of data needed for learning. In addition, in cases of a distribution shift, supervised learning requires extensive relabelling to regain its performance. Methodologies behind unsupervised, self-supervised and weakly-supervised approaches need to be better exploited.
  • Trustworthiness
    AI models need to demonstrate generalizability, interpretability and reproducibility to gain trust. This places a high bar on capabilities AI needs to demonstrate. Currently, most deep learning models are essentially black-boxes, where clinicians have limited understanding of how a model comes to its decision. Further effort into providing evidence for predictions by a model is needed for clinicians to properly weigh its output.
  • Deployment
    Use of AI in the clinic requires additional validation from socio-technical and clinical user experience aspects, to make sure the technology supports and not hinders the clinical workflows.

Despite the above challenges, we are in an exciting era at the interface of AI and retinal imaging, with ample opportunities to innovate and improve the current state of the art. This would allow to leverage the power and enourmous potential of deep learning to make a real impact in ophthalmology by further boosting retinal image analysis capabilities and enabling AI tools to find their way into the market and into the hands of the clinicians world-wide.


Research Group

Hrvoje Bogunovic, PhD

Director of the Christian Doppler Laboratory

Phone: +43 1 40400 73419
Email: hrvoje.bogunovic@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: AKH building (BT 10, 8. floor, room 08.i9.17)

Hrvoje Bogunović obtained his BSc and MSc in Computer Science from the University of Zagreb, Croatia. He obtained his PhD in 2012 from the Universitat Pompeu Fabra (UPF), Barcelona, Spain. For his thesis he worked on medical image segmentation and shape analysis applied to blood vessels in the brain. After graduation he did a postdoc at the Iowa Institute for Biomedical Imaging (IIBI), University of Iowa, US, where he started specializing in computational ophthalmic image analysis. He moved to Medical University of Vienna, Austria in 2015 to work on deep learning for retinal imaging in close collaboration with retinal specialists. As of 2018 he is a Faculty there and as of 2021 a Director of Christian Doppler Lab for Artificial Intelligence in Retina.

His general research interests are in medical image computing and machine learning for healthcare. He is particularly interested in predicting disease progression and in knowledge discovery from large clinical longitudinal imaging datasets..

Research Interests:

  • Medical Image Computing
  • Computational Retinal Image Analysis
  • Machine Learning for Healthcare

Selected Publications: 

D. Romo-Bucheli, U. Schmidt-Erfurth, H. Bogunović: “End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging”. IEEE Journal of Biomedical and Health Informatics, In Press, 2020. doi: 10.1109/JBHI.2020.3000136

U. Schmidt-Erfurth, W-D Vogl, L. Jampol, H. Bogunović: “Application of automated quantification of fluid volumes to anti-VEGF therapy of neovascular age-related macular degeneration”. Ophthalmology, In Press, 2020. doi: 10.1016/j.ophtha.2020.03.010

D. Romo-Bucheli, P. Seeböck, J.I. Orlando, B.S. Gerendas, S.M. Waldstein, U. Schmidt-Erfurth, H. Bogunović: “Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina”. Biomedical Optics Express, vol. 11(1), pp. 346-363, 2020. doi: 10.1364/BOE.379978

H. Bogunović, F. Venhuizen, S. Klimscha, S. Apostolopoulos, A. Bab-Hadiashar, U. Bagci, M.F. Beg, L. Bekalo, Q. Chen, C. Ciller, K. Gopinath,A.K. Gostar, K. Jeon, Z. Ji, S. Ho Kang, D.D. Koozekanani, D. Lu, D. Morley,K.K. Parhi, H. Suk Park, A. Rashno, M. Sarunic, S. Shaikh, J. Sivaswamy,R. Tennakoon, S. Yadav, S. De Zanet, S.M. Waldstein, B.S. Gerendas, C.Klaver, C.I. Sanchez, U. Schmidt-Erfurth: “RETOUCH-The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge”, IEEE Transactionson Medical Imaging, vol 38(8), pp. 1858-1874, 2019. doi: 10.1109/TMI.2019.2901398

P. Seebock, S.M. Waldstein, S. Klimscha, H. Bogunović, T. Schlegl, B.S.Gerendas, R. Donner, U. Schmidt-Erfurth, G. Langs: “Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data”. IEEE Transaction on Medical Imaging, vol. 38(4), pp. 1037-1047, 2019. doi: 10.1109/TMI.2018.2877080

U. Schmidt-Erfurth, A. Sadeghipour, B.S. Gerendas, S.M. Waldstein, H. Bogunović: “Artificial Intelligence in Retina”. Progress in Retinal and Eye Research, vol. 67, pp. 1-29, 2018. doi: 10.1016/j.preteyeres.2018.07.004

U. Schmidt-Erfurth, S.M. Waldstein, S. Klimscha, A. Sadeghipour, X. Hu,B.S. Gerendas, A. Osborne, H. Bogunović: “Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence”. Investigative Ophthalmology & Visual Science, vol. 59, pp. 3199-3208, 2018. doi: 10.1167/iovs.18-24106

H. Bogunović, S.M. Waldstein, T. Schlegl, G. Langs, A. Sadeghipour, X.Liu, B.S. Gerendas, A. Osborne, U. Schmidt-Erfurth. “Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach”. Investigative Ophthalmology & Visual Science, vol.58, pp.3240-3248, 2017. doi: 10.1167/iovs.16-21053

H. Bogunović, A. Montuoro, M. Baratsits, M.G. Karantonis, S. M. Wald-stein, F. Schlanitz, U. Schmidt-Erfurth. “Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging”. Investigative Ophthalmology & Visual Science, vol.58, BIO141-BIO150, 2017. doi: 10.1167/iovs.17-21789.

A. Montuoro, S.M. Waldstein, B.S. Gerendas, U. Schmidt-Erfurth, H. Bogunović: “Joint retinal layer and fluid segmentation in OCT scans of eyes with severe macular edema using unsupervised representation and auto-context”. Biomedical Optics Express, vol. 8(3), pp. 1874-1888, 2017. doi: 10.1364/BOE.8.001874

Julia Mai, MD

Clinical research | PhD Student

Phone: +43 1 40400 79240
Email: julia.mai@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: AKH building (BT 10, 8. floor, room 08.i9.15)

Julia Mai obtained her MD degree at the Ludwig-Maximilians-University (LMU) Munich, Germany in May 2018 and joined the Laboratory for Ophthalmic Image Analysis (OPTIMA) in December 2019. Since April 2020 she is enrolled in the PhD Program for Medical Imaging at the Medical University of Vienna and currently focuses her research on OCT biomarkers in atrophic age-related macular degeneration (AMD). Besides her PhD study, she works in the outpatient clinic at the Department of Ophthalmology and Optometry to gain clinical knowledge and experience.

Her general research interests are in retinal image analysis and morphologic biomarkers predictive of disease progression and treatment response, especially in AMD. She is particularly interested in the application of automated algorithms for detection and quantification of retinal imaging biomarkers.

Research Interests:

  • Retinal image analysis
  • Morphologic imaging biomarkers in retinal diseases
  • Application of automated imaging algorithms

Teresa Finisterra Araújo, PhD

Computational Imaging Research | Postdoctoral Researcher

Phone: +43 1 40400 79750
Email:  teresa.safinisterraaraujo@meduniwien.ac.at

 

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: Rectorate building (BT 88, 2. floor, room 409)

Teresa Araújo received the M.Sc. degree in bioengineering and biomedical engineering from the Faculty of the Engineering, University of Porto (FEUP), Porto, Portugal, in 2016. She obtained her PhD degree in electrical and computer engineering in 2021. For her thesis she worked on diabetic retinopathy grading in color eye fundus images. Since 2014, she has collaborated as a Researcher with the Biomedical Imaging Laboratory, Center for Biomedical Engineering Research (C-BER), Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Porto. As of 2021 she has joined the Christian Doppler Laboratory for Artificial Intelligence in Retina as a postdoc researcher.

Research Interests:

  • computer-aided diagnosis
  • medical image analysis
  • computer vision
  • machine learning
  • deep learning

Selected Publications:

Araújo, T., Aresta, G., Mendonça, L., Penas, S., Maia, C., et al.. DR|GRADUATE: Uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images. Medical Image Analysis, 63:101715, 7 2020c. Doi: 10.1016/j.media.2020.101715

Aresta, G., Araújo, T., Kwok, S., Chennamse.y, S. S., Safwan, M., et al. BACH: Grand challenge on breast cancer histology images. Medical Image Analysis, 56: 122–139, 2019d. doi: 10.1016/j.media.2019.05.010

Araújo, T., Aresta, G., Mendonc¸a, L., Penas, S., Maia, C., et al. Data Augmentation for Improving Proliferative Diabetic Retinopathy Detection in Eye Fundus Images. IEEE Access, 8:182462–182474, 2020b. Doi: 10.1109/ACCESS.2020.3028960

Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., et al. Classification of breast cancer histology images using Convolutional Neural Networks. PloS ONE, 12(6):e0177544, 2017a. doi: 10.1371/journal.pone.0177544

Araújo, T., Mendonça, A. M., and Campilho, A. Parametric modeling based approach for retinal blood vessel caliber estimation in eye fundus images. PLoS ONE, 13(4):1–27, 2018b. doi: 10.1371/journal.pone.0194702

Guilherme Moreira Aresta, PhD

Computational Imaging Research | Postdoctoral Researcher

Phone: +43 1 40400 79750
Email: guilherme.moreiraaresta@meduniwien.ac.at

 

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: Rectorate building (BT 88, 2. floor, room 409)

Guilherme Aresta received his M.Sc. degree in Bioengineering – Biomedical engineering from the Faculty of Engineering of University of Porto, Porto, Portugal in 2016. He later pursued his Ph.D. degree in Electrical and Computer Engineering at the same faculty, having focused on the development of deep learning approaches for medical image analysis and especially lung cancer screening. He collaborated as a researcher with the Biomedical Imaging Laboratory, Center for Biomedical Engineering Research (C-BER) of the Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC) since 2014. Since 2021 he has joined the Christian Doppler Laboratory for Artificial Intelligence in Retina as a postdoc researcher.

His research interests are related to the study of machine learning, deep learning and computer vision techniques for medical image analysis and computer-aided diagnosis.

Selected Publications:

Aresta, G., Araújo, T., Kwok, S.,et al.BACH: Grand challenge on breast cancer histologyimages.Medical Image Analysis, 56:122–139, 2019c. doi: 10.1016/j.media.2019.05.010(first two authors contributed equally)

Aresta, G., Jacobs, C., Araújo, T.,et al.iW-Net: an automatic and minimalistic interactivelung nodule segmentation deep network.Scientific Reports, 9(1):1–9, 2019e. doi: 10.1038/s41598-019-48004-8

Aresta, G., Ferreira, C., Pedrosa, J.,et al.Automatic Lung Nodule Detection CombinedWith Gaze Information Improves Radiologists’ Screening Performance.IEEE journal ofbiomedical and health informatics, 24(10):2894–2901, 2020. doi: 10.1109/JBHI.2020.2976150

Dimitrii Lachinov, MSc

Computational Imaging Research | PhD Student

Phone: +43 1 40400 67600
Email: dimitrii.lachinov@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: Rectorate building (BT 88, 2. floor, room 206)

Dmitrii Lachinov received his BSc and MSc in Computer Science from Lobachevsky State University of Nizhny Novogrod, Russia. During his masters, Dmitrii was an exchange student at Sungkyunkwan University, South Korea. During his studies, Dmitrii was working on heart vessel segmentation and image registration. He defended his MSc thesis on the point set registration in 2018.  After that, he joined Intel as an intern and then as a Deep Learning Research Engineer. Dmitrii contributed to the deep learning single image super-resolution and biomedical image segmentation algorithms to OpenVINO. As of 2019, he is a Ph.D. student in the Medical Imaging program of the Medical University of Vienna.

Dmitrii is focusing on Medical Image Analysis and Computer Vision. His research topics include image segmentation, image registration, longitudinal prediction,  implicit deep learning models, with applications to healthcare.

Research Interests:

  • Medical image analysis
  • Computer Vision
  • Deep Learning

Antoine Rivail, MSc

Computational Imaging Research | PhD Student

Phone: +43 1 40400 67600
Email: antoine.rivail@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: Rectorate building (BT 88, 2. floor, room 206)

Antoine Rivail is a PhD research scientist at the medical University of Vienna. He holds a MSc. degree (2017) with specialisations in Machine learning and autonomous systems at the ISAE-Supaero (France). After a master thesis at IRT Saint-Exupery working on deep learning systems for satellite imagery, he joined the Optima group in 2017 as a PhD student. His work is focused on self-supervised learning and early prediction.

Research Interests:

  • Machine learning
  • Deep learning
  • Medical image analysis
  • Computer Vision

Selected Publications:

Rivail A. et al. (2019) Modeling Disease Progression in Retinal OCTs with Longitudinal Self-supervised Learning. In: Rekik I., Adeli E., Park S. (eds) Predictive Intelligence in Medicine. PRIME 2019. Lecture Notes in Computer Science, vol 11843. Springer, Cham. doi.org/10.1007/978-3-030-32281-6_5

Botond Fazekas, MSc

Computational Imaging Research | PhD Student

Phone: +43 1 40400 67600
Email: botond.fazekas@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: Rectorate building (BT 88, 2. floor, room 206)
 

Botond Fazekas obtained his BSc in Software Engineering and MSc in Computational Intelligence from the Vienna University of Technology. During and after his graduation he worked for several years as a freelancer contractor for different Austrian and German companies leading large-scale software development projects as a software architect, besides working on research projects focusing on predictive maintenance for locomotives and high-speed computer vision solutions for train pantograph certification and safety evaluation. He joined the research group as a research engineer in 2020 to pursue an academic career and he started his PhD studies in 2021 under the supervision of ‪Hrvoje Bogunović. ‬

Research Interests:

  • Medical Image Computing
  • Machine Learning interpretability 

Selected Publications:
 

Hana Jebril, MSc

Computational Imaging Research | PhD Student

Phone: +43 1 40400 67600
Email: hana.jebril@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: Rectorate building (BT 88, 2. floor, room 206)

Hana Jebril obtained her BEng degree in computer and communication engineering from Al-Azhar Univesity- Palestine. Additionally, she completed her MSc degree in ICT for Internet and Multimedia from the University of Padova – Italy in Nov. 2020 with a GPA of 110/110 cum laude. For her master’s thesis, she worked on the semiconductor time-series data analysis in iDev40 EU project at KAI Kompetenzzentrum Automobil- u Industrieelektronik GmbH. After completing her MSc, she continued to work in the same company till the end of the iDev project. In May 2021 she started her PhD at the Medical University of Vienna as a researcher in the Christian Doppler Laboratory for Artificial Intelligence in Retina working on computational imaging methods and machine learning.

Research Interests:

  • Machine learning algorithms
  • Deep learning modelling
  • Data Analysis

Ghaith Arfaoui, Dipl. -Ing.

Computational Imaging Research | Master's Student

Phone: +43 1 40400 78930
Email: ghaith.arfaoui@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: AKH building (BT 24, 4. floor, room 04.P0.11)

Ghaith Arfaoui obtained a degree in mathematics and physics from the Preparatory Institute for Engineering Studies of Tunis and a bachelor in petroleum engineering from Montanuniversität Leoben as part of a double degree program in 2016. He also obtained a master’s degree in petroleum engineering from Montanuniversität Leoben in 2018. He is currently pursuing a master’s degree in Data Science at the Technical University of Vienna. As part of his master thesis project, he is working on active deep learning in the context of retinal image segmentation.

Research Interests:

  • Machine Learning
  • Active Learning
  • Image Segmentation

Claudius-Daniel Ciupe, BSc

Computational Imaging Research | Master's Student

Phone: +43 1 40400 66220
Email: claudius-daniel.ciupe@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: Rectorate building (BT 88, 2. floor, room 407)

Claudius-Daniel Ciupe obtained his BSc in Medical Informatics from the Technical University of Vienna. He is currently obtaining his masters degree in Medical Informatics at the Medical University of Vienna, where for his thesis, he joined the Christian Doppler Laboratory for Artificial Intelligence in Retina. In his work, he uses machine learning for automated OCT image quality analysis.

His general research interests are in medical image analysis, computer vision and machine learning with applications in healthcare.

Research Interests:

  • Computational Retinal Image Analysis
  • Machine Learning for Healthcare

Christoph Wimmer, MSc

Computational Imaging Research | Master's Student

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Christoph Wimmer obtained his BSc in Business Administration from the Karl Franzens Universität Graz and his MSc in Finance and Accounting from the WU with his master thesis “Cryptocurrency Litecoin (LTC): Risk and Dependency Assessment”. He is currently pursuing his MSc in Data Science at the TU Wien. He started to work at the Medical University of Vienna, Austria in 2020 as a software developer, and has later joined the Christian Doppler Laboratory for Artificial Intelligence in Retina and is working on his master thesis “Fully Convolutional Boundary Regression with constraints for Retina OCT Segmentation”.

His general research interests are medical image analysis, computer vision, machine learning with applications in healthcare and risk management for cryptocurrencies. He is particularly interested in machine learning for healthcare related segmentation.

Research Interests:

  • Medical Image Computing
  • Computational Retinal Image Analysis
  • Machine Learning for Healthcare
  • Risk Management for Cryptocurrencies

Georg Faustmann, MSc

Software Development

Phone: +43 1 40400 78900
Email: georg.faustmann@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: AKH building (BT 24, 4. floor, room 04.P0.03)

Georg Faustmann obtained his MSc in logic and computation (computer science) at the TU Wien. His thesis was about optimizing production processes in the industry via machine learning. This thesis was done at the Christian Doppler Laboratory for Artificial Intelligence and Optimization for Planning and Scheduling. Before his studies, he programmed microcontrollers for entrance systems in trains. During his time at the university, he worked at Google Summer of Code and Siemens on a project about smart grids and as a teaching assistant for a functional programming course. As a research engineer at the Christian Doppler Laboratory for Artificial Intelligence in Retina, he integrates state-of-the-art research into applications used by medical staff.

Interested in:

  • Full-stack web development
  • Supervised Machine Learning
  • Functional programming

Omar Ismail, BSc

Software Development

Phone: +43 1 40400 78900
Email: omar.ismail@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: AKH building (BT 24, 4. floor, room 04.P0.03)

Omar Ismail is completing his MSc in Computer Vision at the Technical University of Vienna, Austria. He has cooperated with the Medical University of Vienna and the AKH within the framework of his bachelor’s degree. He has also partnered with Stanford University , working on a project focusing on brain shift visualization.

His general research interests include image analysis, computer vision, machine/deep learning and pattern recognition.

Hamza Mohamed

Reading Team

Phone: +43 1 40400 67640
Email: hamza.mohamed@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: AKH building (BT 10, 8. floor, room 08.H2.01)

Hamza Mohamed studies Medicine at the Medical University of Vienna, Austria and joined the Christian Doppler Laboratory for Artificial Intelligence in Retina as a Reader in March 2021. His  interests lie in Ophthalmology, Microsurgery and Neuroscience. Currently he is writing on his thesis in the field of Ophthalmology.

He has a technical background as he graduated from a higher technical college with a focus on structural engineering.

Jasenka Palavric

Reading Team

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: AKH building (BT 10, 8. floor, room 08.H2.01)

Jasenka graduated from the Business School in Vienna in 1999.  Subsequently she has worked as an assistant in the office support area, fashion industry and in an event agency. After parental leave, seeking new challenges, she joined the Optima Reading team in September 2013, she also continues with this work in the Christian Doppler Laboratory for Artificial Intelligence in Retina.  

Paulína Šujanová, PhD

Administrative | Project Manager

Phone: +43 1 40400 73419
Email: paulina.sujanova@meduniwien.ac.at

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna
Spitalgasse 23
1090 Vienna, Austria

Office: AKH building (BT 10, 8. floor, room 08.i9.17)

Paulína Šujanová has studied at the Slovak University of Technology in Bratislava architecture (Ing. arch.), and has a PhD from the same university in the Theory and Structures of Buildings  focusing on Building Physics. During her studies she was awarded multiple mobility grants resulting in internships at the EMPA in Switzerland, University of Liechtenstein in Liechtenstein and the KU Leuven in Belgium (among other). Before joining the Christian Doppler Laboratory for Artificial Intelligence in Retina she worked as a project consultant at the European Alliance for Innovation (Belgium/Slovakia), and as a project manager at the University of Ss. Cyril and Methodius in Trnava (Slovakia), and externally for the KU Leuven (Belgium).


Publications

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Vacant positions

We welcome applications for:


Funding

Christian Doppler Laboratory for Artificial Intelligence in Retina – “CD AIR” is supported by the Christian Doppler Research Association in collaboration with Heidelberg Engineering GmbH.

In Christian Doppler Laboratories, application-oriented basic research is pursued at a high level, and expert scientists cooperate with innovative companies. The Christian Doppler Research Association is an international best practice example for promoting this collaboration.

Christian Doppler Laboratories are financed jointly by the public purse and the participating companies. The most important public sponsor is the Federal Ministry for Digital and Economic Affairs (BMDW).


Contact and address

Christian Doppler Laboratory for Artificial Intelligence in Retina
Department of Ophthalmology and Optometry
Medical University of Vienna

Spitalgasse 23
1090 Vienna, Austria
Phone: +43 1 40400 73419

Imprint

Reseponsible for contents:
Hrvoje Bogunovic, PhD
Department of Ophthalmology
Medical University of Vienna
Währinger Guertel 18-20
1090 Vienna