Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research
By Prasanna Porwal, S. Pachade, Ravi Kamble, M. Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, Fabrice Mériaudeau. International Conference on Data Technologies and Applications, 2018
1,032 citations recorded by the source corpus.
Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting the working-age population in the world. Recent research has given a better understanding of the requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer-aided disease diagnosis in retinal image analysis could ease mass screening of populations with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. Diverse and representative retinal image sets are essential for developing and testing digital screening programs and the automated algorithms at their core. To the best of our knowledge, IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population. It constitutes typical diabetic retinopathy lesions and normal retinal structures annotated at a pixel level. The dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image. This makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.
Subfields: Health Information Management, Ophthalmology, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/c7b159631a98540b66fb0f846ed6ae830ac3998e
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In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- Embedding Human Knowledge into Deep Neural Network via Attention Map (2019, methodology): “For this disease recognition, we use the Indian Diabetic Retinopathy Image Dataset (IDRiD) (Porwal et al., 2018).”
- 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].”
- REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019, neutral): “ segmentation network to predict a glaucoma likelihood None VRT Full image with custom mask for attention Custom (Son et al., 2018) Kaggle (Kaggle, 2015), MESSIDOR (Decencire et al., 2014) and IDRiD (Porwal et al., 2018) Attention guided model trained on public data sets of DR images, weakly labelle”
- Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives (2022, methodology): “The disease level has been investigated in Liu et al. (2021h, 2022a), in which the Kaggle Diabetic Retinopathy (KDR)1 is used as the source domain, and the recent Indian Diabetic Retinopathy Image Dataset (IDRiD) dataset (Porwal et al., 2018) is used as the target domain.”