ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images

• URL: http://arxiv.org/abs/2202.07983v2
• Date: Fri, 18 Feb 2022 03:42:17 GMT
• Title: ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
• Authors: Huihui Fang, Fei Li, Huazhu Fu, Xu Sun, Xingxing Cao, Fengbin Lin, Jaemin Son, Sunho Kim, Gwenole Quellec, Sarah Matta, Sharath M Shankaranarayana, Yi-Ting Chen, Chuen-heng Wang, Nisarg A. Shah, Chia-Yen Lee, Chih-Chung Hsu, Hai Xie, Baiying Lei, Ujjwal Baid, Shubham Innani, Kang Dang, Wenxiu Shi, Ravi Kamble, Nitin Singhal, Jos\'e Ignacio Orlando, Hrvoje Bogunovi\'c, Xiulan Zhang, Yanwu Xu
• Abstract summary: We set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM) for the first time, held as a satellite event of the I SBI 2020 conference. ADAM challenge consists of four tasks which cover the main topics in detecting AMD from fundus images. This paper introduces the challenge, dataset, and evaluation methods, as well as summarizes the methods and analyzes the results of the participating teams of each task.
• Score: 44.212866865895485
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance as the vision loss caused by AMD is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. \textcolor{red}{Recently, some algorithms based on deep learning had been developed for fundus image analysis and automatic AMD detection. However, a comprehensive annotated dataset and a standard evaluation benchmark are still missing.} To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM) for the first time, held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main topics in detecting AMD from fundus images, including classification of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. The ADAM challenge has released a comprehensive dataset of 1200 fundus images with the category labels of AMD, the pixel-wise segmentation masks of the full optic disc and lesions (drusen, exudate, hemorrhage, scar, and other), as well as the location coordinates of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models. During the ADAM challenge, 610 results were submitted for online evaluation, and finally, 11 teams participated in the onsite challenge. This paper introduces the challenge, dataset, and evaluation methods, as well as summarizes the methods and analyzes the results of the participating teams of each task. In particular, we observed that ensembling strategy and clinical prior knowledge can better improve the performances of the deep learning models.

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