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Found 8 papers, 1 papers with code
Spatiotemporal Representation Learning for Short and Long Medical Image Time Series
Moreover, tracking longer term developments that occur over months or years in evolving processes, such as age-related macular degeneration (AMD), is essential for accurate prognosis.
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs
Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models.
Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT
In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression.
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data.
Morph-SSL: Self-Supervision with Longitudinal Morphing to Predict AMD Progression from OCT
We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan.
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly.
Metadata-enhanced contrastive learning from retinal optical coherence tomography images
This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time.
Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network
We have developed and trained a convolutional neural network to automatically and simultaneously segment optic disc, fovea and blood vessels.
