  This investigation reports on the results of convolutional neural networks
developed for the recently introduced PathologicAL Myopia (PALM) dataset, which
consists of 1200 fundus images. We propose a new Optic Nerve Head (ONH)-based
prediction enhancement for the segmentation of atrophy and fovea. Models
trained with 400 available training images achieved an AUC of 0.9867 for
pathological myopia classification, and a Euclidean distance of 58.27 pixels on
the fovea localization task, evaluated on a test set of 400 images. Dice and F1
metrics for semantic segmentation of lesions scored 0.9303 and 0.9869 on optic
disc, 0.8001 and 0.9135 on retinal atrophy, and 0.8073 and 0.7059 on retinal
detachment, respectively. Our work was acknowledged with an award in the
context of the "PathologicAL Myopia detection from retinal images" challenge
held during the IEEE International Symposium on Biomedical Imaging (April
2019). Considering that (pathological) myopia cases are often identified as
false positives and negatives in classification systems for glaucoma, we
envision that the current work could aid in future research to discriminate
between glaucomatous and highly-myopic eyes, complemented by the localization
and segmentation of landmarks such as fovea, optic disc and atrophy.
