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Found 6 papers, 1 papers with code
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression
This work proposes a novel framework for analyzing disease progression using time-aware neural ordinary differential equations (NODE).
L-MAE: Longitudinal masked auto-encoder with time and severity-aware encoding for diabetic retinopathy progression prediction
Our results demonstrated the relevancy of both time-aware position embedding and masking strategies based on disease progression knowledge.
Longitudinal Self-supervised Learning Using Neural Ordinary Differential Equation
In recent years, a novel class of algorithms has emerged with the goal of learning disease progression in a self-supervised manner, using either pairs of consecutive images or time series of images.
LMT: Longitudinal Mixing Training, a Framework to Predict Disease Progression from a Single Image
Our framework, Longitudinal Mixing Training (LMT), can be considered both as a regularizer and as a pretext task that encodes the disease progression in the latent space.
Segmentation, Classification, and Quality Assessment of UW-OCTA Images for the Diagnosis of Diabetic Retinopathy
Diabetic Retinopathy (DR) is a severe complication of diabetes that can cause blindness.
Mapping the ocular surface from monocular videos with an application to dry eye disease grading
With a prevalence of 5 to 50%, Dry Eye Disease (DED) is one of the leading reasons for ophthalmologist consultations.
