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

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 InnaniKang Dang, Wenxiu Shi, Ravi Kamble, Nitin Singhal, Ching Wei Wang, Shih Chang Lo, Jose Ignacio Orlando, Hrvoje Bogunovic, Xiulan Zhang, Yanwu Xu

研究成果: Article同行評審

摘要

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 this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

原文English
期刊IEEE Transactions on Medical Imaging
DOIs
出版狀態Accepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • 軟體
  • 放射與超音波技術
  • 電腦科學應用
  • 電氣與電子工程

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