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Found 5 papers, 1 papers with code
Time-Equivariant Contrastive Learning for Degenerative Disease Progression in Retinal OCT
Contrastive pretraining provides robust representations by ensuring their invariance to different image transformations while simultaneously preventing representational collapse.
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.
TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes
Recent contrastive learning methods achieved state-of-the-art in low label regimes.
Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning
Longitudinal imaging is capable of capturing the static ana\-to\-mi\-cal structures and the dynamic changes of the morphology resulting from aging or disease progression.
