Search Results for author:
Found 5 papers, 1 papers with code
A deep learning framework for the detection and quantification of drusen and reticular pseudodrusen on optical coherence tomography
Methods - A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen.
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model.
Iterative Augmentation of Visual Evidence for Weakly-Supervised Lesion Localization in Deep Interpretability Frameworks: Application to Color Fundus Images
We show that the augmented visual evidence of the predictions highlights the biomarkers considered by experts for diagnosis and improves the final localization performance.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history
Participants: 409 CFIs of 238 eyes with GA from the Rotterdam Study (RS) and the Blue Mountain Eye Study (BMES) for model development, and 5, 379 CFIs of 625 eyes from the Age-Related Eye Disease Study (AREDS) for analysis of GA growth rate.
Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration
Purpose: To validate the performance of a commercially-available, CE-certified deep learning (DL) system, RetCAD v. 1. 3. 0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in color fundus (CF) images on a dataset with mixed presence of eye diseases.
