Modeling Disease Progression in Retinal OCTs with Longitudinal Self-supervised Learning
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Rivail et al. proposed a self-supervised approach to model disease progression by estimating the time interval between pairs of optical coherence tomography (OCT) scans from the same patient.
“Rivail et al. proposed a self-supervised approach to model disease progression by estimating the time interval between pairs of optical coherence tomography (OCT) scans from the same patient104.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [104]
Cites
Adam: A Method for Stochastic Optimization
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SIGNATURE VERIFICATION USING A “SIAMESE” TIME DELAY NEURAL NETWORK
BROMLEY, J., BENTZ, J. W., BOTTOU, L., GUYON, I., LECUN, Y., MOORE, C., SÄCKINGER, E., & SHAH, R. (1994) · Series in Machine Perception and Artificial Intelligence · doi:10.1142/9789812797926_0003 · Cited by 2,350 · see citing works
Unsupervised Representation Learning by Sorting Sequences
Lee, H.-Y., Huang, J., Singh, M., & Yang, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.79 · Cited by 529 · see citing works
The Development, Commercialization, and Impact of Optical Coherence Tomography
Fujimoto, J. G., & Swanson, E. A. (2016) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.16-19963 · Cited by 477 · see citing works
Series in Machine Perception and Artificial Intelligence
(2012) · Series in machine perception and artificial intelligence · doi:10.1142/smpai · Cited by 371 · see citing works
Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence
Schmidt-Erfurth, U. M., Waldstein, S. M., Riedl, S., Sadeghipour, A., Hu, X., Gerendas, B. S., Osborne, A. R., & Bogunović, H. (2018) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.18-24106 · Cited by 216 · see citing works
Self-citationChen, X., Niemeijer, M., Zhang, L., Lee, K., Abràmoff, M. D., & Milan Sonka, A. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2191302 · Cited by 199 · see citing works
Vogl, W., Waldstein, S. M., Gerendas, B. S., Schmidt-Erfurth, U. M., & Langs, G. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2700213 · Cited by 42 · see citing works
Self-citationVogl, W., Waldstein, S. M., Gerendas, B. S., Schlegl, T., Langs, G., & Schmidt-Erfurth, U. M. (2017) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.17-21878 · Cited by 39 · see citing works
Self-citationEvent time analysis of longitudinal neuroimage data
Sabuncu, M. R., Bernal-Rusiel, J. L., Reuter, M., Greve, D. N., & Fischl, B. R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.015 · Cited by 31 · see citing works
Efficient Gaussian Process-Based Modelling and Prediction of Image Time Series
Lorenzi, M., Ziegler, G., Alexander, D. C., et al. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_49 · Cited by 22 · see citing works
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Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works
“Rivail et al. proposed a self-supervised approach to model disease progression by estimating the time interval between pairs of optical coherence tomography (OCT) scans from the same patient 104 .”
Background
- Authors
- Rivail, A., Schmidt-Erfurth, U., Vogl, W.-D., Waldstein, S. M., Riedl, S., Grechenig, C., Wu, Z., & Bogunovic, H.
- Affiliations
- Medical University of Vienna (AT)
- The University of Melbourne (AU)
- Corresponding author
- Rivail, Antoine
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 44-52
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2979924865
- Cited by
- 25
- References
- 11
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 7 · 2023: 6 · 2022: 3 · 2021: 2 · 2020: 1
- FWCI
- 2.27
- Type
- conference paper
- Language
- en
- Update notices
- correction (2019-10-10)
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- disease progression modeling, self-supervised learning, representation learning, age-related macular degeneration, irregularly sampled data, transfer learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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