The DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol

By T. Kauppi, V. Kalesnykiene, Joni-Kristian Kamarainen, Lasse Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, Heikki Kälviäinen, J. Pietilä. 2007

916 citations recorded by the source corpus.

Subfields: Health Information Management, 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): “To evaluate FGVis, the DiaretDB1 dataset [25] is used containing 89 fundus images with different lesion types, ground truth marked by four experts.”
RIM-ONE: An open retinal image database for optic nerve evaluation (2011, background): “Diaretdb0 [20] and diaretdb1 [21] are also related to the development of algorithms for diabetic retinopathies, and they contain some elliptical optic disc segmentations.”
A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction (2015, methodology): “For analysing the accuracy of our method on different set of images, we have used publicly available DIARETDB1 [11] and e-ophthaEX [12] dataset.”
Deep image mining for diabetic retinopathy screening (2016, methodology): “Given a target lesion type, Kauppi et al. (2007) proposed a standardized procedure to evaluate the performance of a lesion detector in the DiaretDB1 dataset.”
FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE (2014, methodology): “This is an important difference with respect to other databases, such as DIARETDB1 and e-ophtha.”
Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images (2017, methodology): “We use the Kaggle dataset for training and evaluate our lesion localization approach on the dataset DiaretDB1 [28].”
An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading (2012, methodology): “We have evaluated the MA detection capabilities of the proposed method in the ROC competition for MA detectors [13], as well as on a publicly available [21] and a private database.”
Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets (2011, neutral): “2004), ARIA (ARIA, 2006), DIARETDB1 (Kauppi et al., 2007), MESSIDOR (Messidor, 2010) and ROC (Niemeijer et al.”
Fundus Image Classification Using VGG-19 Architecture with PCA and SVD (2018, methodology): “Kälviäinen and Uusitalo [18] introduced an evaluation technique to analyze the results of the retinal fundus images obtained by the implementation of different architectures, models, and frameworks.”
Identification and classification of microaneurysms for early detection of diabetic retinopathy (2013, methodology): “We have used two publicly available retinal image databases namely DIARETDB0 [27] and DIARETDB1 [28] for evaluation purposes.”
Transformers in Medical Imaging: A Survey (2022, neutral): “Image ReportDataset Number Modality View* Length* Language Cases IU X-ray [341] 7,470 X-Ray 2 32.5 Eng. 2,955 MIMIC-CXR [352] 377,110 X-Ray 1 53.2 Eng. 276,778 PadChest [355] 160,868 X-Ray 2 - Es 22,710 CX-CHR [356] 45,598 X-Ray 2 66.9 Zh 40,410 DIARETDB1 [357] 89 CFP 1 - Eng 89 MESSIDOR [261] 1,200”
Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation (2014, background): “It involves a progressive degeneration of the optic nerve fibers.”
Computer Methods and Programs in Biomedicine (neutral): “[68] 40 CFP ARIA [137] 143 CFP CHASE [138] 28 CFP HRF [139] 45 CFP IMAGERET [140] 219 CFP MESSIDOR 1200 CFP ( http://messidor.crihan.fr ) REVIEW [141] 16 CFP ROC [142] 100 CFP VICAVR 58 CFP ( http://www.varpa.es ) VAMPIRE 8 FA ( http://vampire.computing . dundee.ac.uk) CASDQEF [143] Coronary 48 CTA…”