AI-Based Retinal Image Analysis Research Group
AI-Based Retinal Image Analysis Research Group
Disciplines
Computer Sciences (80%); Clinical Medicine (20%)
Keywords
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Ophthalmology,
Medical Image Analysis,
Machine Learning,
Deep Learning,
Retina,
Optical coherence tomography
Any disease of the retina at the back of the eye directly impacts visual performance and readily puts vision at risk often leading to practical blindness. For this reason, we need better understanding of retinal disease and their progression patterns to find good retinal treatment solutions. Personalized medicine is an emerging approach where medical decisions and therapies are being tailored to the individual patient. Today, innovations in medical imaging allow an extraordinarily detailed view into one`s health condition. The introduction of optical coherence tomography (OCT) imaging provides a view of the retina in three-dimensions and in very fine detail. The analysis of the sheer volume of information about patients, disease progression and OCT images even exceeds the human capabilities. In the last decade, artificial intelligence (AI) has revolutionized various fields of science in an unprecedented manner. There is relentless pressure and expectation to deploy AI in medicine, especially in image-intensive branches. In Ophthalmology, it has already achieved super-human performance in image diagnosis. Nevertheless, despite initial successes, most of AIs enormous potential is still to be realized and ophthalmology stands to immensely benefit from it. This Research Group has an overarching goal to identify populations of similar retinal patients to improve our understanding of retinal disease and treatment outcomes of an individual patient. We focus on developing, improving and applying AI methods to analyze OCT images of retina and we investigate machine learning methods that can identify new OCT imaging patterns and provide individual prognosis of disease advance. The Group is composed of four world-class and pioneering researchers from the Medical University of Vienna (Ursula Schmidt-Erfurth and Hrvoje Bogunovic) with expertise in ophthalmology and medical image analysis, and the Johannes Kepler University (Sepp Hochreiter and Günter Klambauer) with expertise in AI. They are joining their complimentary expertise with the goal of introducing AI- based personalized medicine into the management of the leading eye diseases of modern times.
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consortium member (01.06.2021 -)
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consortium member (01.06.2021 -)
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consortium member (01.06.2021 -)
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consortium member (01.06.2021 -)
- Medizinische Universität Wien
Research Output
- 27 Citations
- 6 Publications
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2025
Title Combining Bayesian and Evidential Uncertainty Quantification for Improved Bioactivity Modeling DOI 10.1021/acs.jcim.5c01597 Type Journal Article Author Khalil B Journal Journal of Chemical Information and Modeling Pages 13057-13069 Link Publication -
2025
Title Shape Prior for Quality Assessment in OCTA via Denoising Autoencoders at the Segmentation Level DOI 10.1109/access.2025.3625745 Type Journal Article Author Jebril H Journal IEEE Access Pages 187467-187476 Link Publication -
2024
Title 3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression From Longitudinal OCTs DOI 10.1109/tmi.2024.3391215 Type Journal Article Author Emre T Journal IEEE Transactions on Medical Imaging Pages 3200-3210 Link Publication -
2024
Title Morph-SSL: Self-Supervision With Longitudinal Morphing for Forecasting AMD Progression From OCT Volumes DOI 10.1109/tmi.2024.3390940 Type Journal Article Author Chakravarty A Journal IEEE Transactions on Medical Imaging Pages 3224-3239 Link Publication -
2024
Title Improving Clinical Predictions with Multi-Modal Pre-training in Retinal Imaging DOI 10.1109/isbi56570.2024.10635447 Type Conference Proceeding Abstract Author Sükei E Pages 1-5 -
2024
Title Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection DOI 10.1016/j.media.2024.103104 Type Journal Article Author Seeböck P Journal Medical Image Analysis Pages 103104 Link Publication