  In this paper we present algorithms to diagnosis Pathological Myopia (PM) and
detection of retinal structures and lesions such asOptic Disc (OD), Fovea,
Atrophy and Detachment. All these tasks were performed in fundus imaging from
PM patients and they are requirements to participate in the Pathologic Myopia
Challenge (PALM). The challenge was organized as a half day Challenge, a
Satellite Event of The IEEE International Symposium on Biomedical Imaging in
Venice Italy.Our method applies different Deep Learning techniques for each
task. Transfer learning is applied in all tasks using Xception as the baseline
model. Also, some key ideas of YOLO architecture are used in the Optic Disc
segmentation algorithm pipeline. We have evaluated our model's performance
according the challenge rules in terms of AUC-ROC, F1-Score, Mean Dice Score
and Mean Euclidean Distance. For initial activities our method has shown
satisfactory results.
