  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 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 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.
