IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

By Prasanna Porwal, S. Pachade, M. Kokare, Girish Deshmukh, Jaemin Son, Woong Bae, Lihong Liu, Jianzong Wang, Xinhui Liu, Liangxin Gao and 47 more. Medical Image Analysis, 2020

366 citations recorded by the source corpus.

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 "Diabetic Retinopathy - Segmentation and Grading" 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.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/617a52776f261924f24615e2cdebf4d2172e7d9e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Fabrice Mériaudeau
2. Huan Fu
3. Kai Wang
4. Wang Bo
5. Chunyu Hu
6. Hanruo Liu
7. Hong Kang
8. Tao Li

In Ophthalmology

1. Huan Fu
2. Kai Wang
3. Tao Li

In Radiology, Nuclear Medicine and Imaging

1. Fabrice Mériaudeau
2. Huan Fu
3. Kai Wang
4. Tao Li

Citations in the corpus

• Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research (2018, methodology): “The data challenge was hosted on Grand Challenges in Biomedical Imaging Platform [20].”
• Applications of Deep Learning in Fundus Images: A Review (2021, methodology): “Porwal et al. (2020) described the IDRiD dataset and outlined the setup of the challenge “Diabetic”