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Found 10 papers, 4 papers with code
Delving into Out-of-Distribution Detection with Medical Vision-Language Models
Recent advances in medical vision-language models (VLMs) demonstrate impressive performance in image classification tasks, driven by their strong zero-shot generalization capabilities.
Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?
DINOv2-large model outperformed RETFound in detecting diabetic retinopathy (AUROC=0. 850-0. 952 vs 0. 823-0. 944, across three datasets, all P<=0. 007) and multi-class eye diseases (AUROC=0. 892 vs. 0. 846, P<0. 001).
Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?
Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications.
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting
DINORET and BE DINORET demonstrated competitive performance on retinal imaging tasks, with the block expanded model achieving the highest scores on most datasets.
Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model
In zero-shot scenarios, RetiZero achieves Top-5 accuracies of 0. 843 for 15 diseases and 0. 756 for 52 diseases.
VAFO-Loss: VAscular Feature Optimised Loss Function for Retinal Artery/Vein Segmentation
Estimating clinically-relevant vascular features following vessel segmentation is a standard pipeline for retinal vessel analysis, which provides potential ocular biomarkers for both ophthalmic disease and systemic disease.
Learning to Address Intra-segment Misclassification in Retinal Imaging
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity.
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME.
Forecasting Future Humphrey Visual Fields Using Deep Learning
Methods: All datapoints from consecutive 24-2 HVFs from 1998 to 2018 were extracted from a University of Washington database.
Deep learning for predicting refractive error from retinal fundus images
Mean absolute error (MAE) of the algorithm's prediction compared to the refractive error obtained in the AREDS and UK Biobank.
