EyePACS: An Adaptable Telemedicine System for Diabetic Retinopathy Screening

By Jorge A Cuadros, G. Bresnick. 2009

391 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks (2019, methodology): “Of the publicly available 88,702 images [15] from Eye-PACS [10], we us 80% for training and 20% for validation for a classiﬁer with binary outcome (referable diabetic retinopathy (RDR) vs. non-RDR) which is later used for the weakly-supervised localization.”
Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research (2021, background): “In the past few decades, larger amount of color fundus images of diabetic patients were collected through Diabetic Retinopathy (DR) screening programs [5–7].”
Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images (2017, methodology): “Kaggle Dataset: The dataset [30] provided by EyePACS [29] contains 88,702 color fundus images of which 80% were used for training and 20% for validation.”
Automated Identification of Diabetic Retinopathy Using Deep Learning. (2017, methodology): “We derived our predictive algorithm from a data set of 75 137 color fundus images obtained from the EyePACS public data set (EyePACS LLC, Berkeley, CA).(17) The images represented a heterogeneous cohort of patients with all stages of DR.”
DREAM: Diabetic Retinopathy Analysis Using Machine Learning (2014, background): “Telemedicine, with distributed remote retinal fundus imaging and grading at either local primary care ofﬁces or centralized grading remotely by eye care specialists, has increased access to screening and follow-up necessary treatment [4].”
Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research (2018, methodology): “The diabetic retinal images were classiﬁed into separate groups ranging from 0 (No apparent DR) to 4 (Severe DR) according to the International Clinical Diabetic Retinopathy Scale [24], similar to existing Kaggle DR Dataset [25].”
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, methodology): “This necessity has led several research groups to develop and share retinal image datasets, namely Messidor ( Decencière et al., 2014 ), Kaggle ( Cuadros and Bresnick, 2009 ), ROC ( Niemeijer et al., 2010 ), E-Ophtha ( Decencière et al., 2013 ), DiaretDB ( Kauppi et al., 2012 ), DRIVE ( van…”
Transformers in Medical Imaging: A Survey (2022, background): “LAT [260] Eye Fundus 2D Messidor-1 [261] Messidor-2 [261] EyePACS [262] AuC Kappa Hybrid Formulate lesion discovery as a weakly supervised lesion localization problem via a transformer decoder.”