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Found 8 papers, 5 papers with code
Applicability of oculomics for individual risk prediction: Repeatability and robustness of retinal Fractal Dimension using DART and AutoMorph
In GRAPE, Pearson/Spearman correlation (first and next visit) was 0. 7479/0. 7474 for DART, and 0. 7109/0. 7208 for AutoMorph (all p<0. 0001).
Choroidalyzer: An open-source, end-to-end pipeline for choroidal analysis in optical coherence tomography
We analysed segmentation agreement (AUC, Dice) and choroid metrics agreement (Pearson, Spearman, mean absolute error (MAE)) in internal and external test sets.
QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models
For this task, we present a second model, QuickQual MEga Minified Estimator (QuickQual-MEME), that consists of only 10 parameters on top of an off-the-shelf Densenet121 and can distinguish between gradable and ungradable images with an accuracy of 89. 18% (AUC: 0. 9537).
An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography
Results: DeepGPET achieves excellent agreement with GPET on data from 3 clinical studies (AUC=0. 9994, Dice=0. 9664; Pearson correlation of 0. 8908 for choroidal thickness and 0. 9082 for choroidal area), while reducing the mean processing time per image on a standard laptop CPU from 34. 49s ($\pm$15. 09) using GPET to 1. 25s ($\pm$0. 10) using DeepGPET.
Robust and efficient computation of retinal fractal dimension through deep approximation
Thus, methods for calculating retinal traits tend to be complex, multi-step pipelines that can only be applied to high quality images.
Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions
Previous studies showed that deep learning (DL) models are effective for detecting retinal disease in UWF images, but primarily considered individual diseases under less-than-realistic conditions (excluding images with other diseases, artefacts, comorbidities, or borderline cases; and balancing healthy and diseased images) and did not systematically investigate which regions of the UWF images are relevant for disease detection.
Global explainability in aligned image modalities
We propose the pixel-wise aggregation of image-wise explanations as a simple method to obtain label-wise and overall global explanations.
Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models.
