@article{scholars14070,
            year = {2020},
             doi = {10.1016/j.media.2019.101561},
            note = {cited By 154},
          volume = {59},
         journal = {Medical Image Analysis},
       publisher = {Elsevier B.V.},
           title = {IDRiD: Diabetic Retinopathy {\^a}?? Segmentation and Grading Challenge},
        keywords = {Computer aided analysis; Deep learning; Eye protection; Grading; Image analysis; Medical imaging; Ophthalmology; Vision, Challenge; Clinical information; Diabetic retinopathy; Lesion segmentations; Machine learning approaches; Medical professionals; Retinal image analysis; Scientific community, Computer aided diagnosis, Article; deep learning; diabetic retinopathy; disease severity; human; image analysis; image segmentation; optic disk; priority journal; retina; retina fovea; retina image; screening; automated pattern recognition; computer assisted diagnosis; diabetic retinopathy; diagnostic imaging; information processing; photography; procedures, Datasets as Topic; Deep Learning; Diabetic Retinopathy; Diagnosis, Computer-Assisted; Humans; Image Interpretation, Computer-Assisted; Pattern Recognition, Automated; Photography},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074152892&doi=10.1016\%2fj.media.2019.101561&partnerID=40&md5=f325251c527232a54b57a535314b7dbd},
        abstract = {Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on {\^a}??Diabetic Retinopathy {\^a}?? Segmentation and Grading{\^a}?? was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular. {\^A}{\copyright} 2019 Elsevier B.V.},
            issn = {13618415},
          author = {Porwal, P. and Pachade, S. and Kokare, M. and Deshmukh, G. and Son, J. and Bae, W. and Liu, L. and Wang, J. and Liu, X. and Gao, L. and Wu, T. and Xiao, J. and Wang, F. and Yin, B. and Wang, Y. and Danala, G. and He, L. and Choi, Y. H. and Lee, Y. C. and Jung, S.-H. and Li, Z. and Sui, X. and Wu, J. and Li, X. and Zhou, T. and Toth, J. and Baran, A. and Kori, A. and Chennamsetty, S. S. and Safwan, M. and Alex, V. and Lyu, X. and Cheng, L. and Chu, Q. and Li, P. and Ji, X. and Zhang, S. and Shen, Y. and Dai, L. and Saha, O. and Sathish, R. and Melo, T. and Ara{\~A}?jo, T. and Harangi, B. and Sheng, B. and Fang, R. and Sheet, D. and Hajdu, A. and Zheng, Y. and Mendon{\~A}{\S}a, A. M. and Zhang, S. and Campilho, A. and Zheng, B. and Shen, D. and Giancardo, L. and Quellec, G. and M{\~A}{\copyright}riaudeau, F.}
}