眼底图像中的自动病变分割和病理性近视分类

在本文中，我们提出了诊断病理性近视（PM）以及检测视网膜结构和病变的算法，例如视盘（OD），中央凹，萎缩和分离。所有这些任务都是在PM患者的眼底成像中执行的，它们是参加病理性近视挑战赛（PALM）的要求。..

Automatic lesion segmentation and Pathological Myopia classification in fundus images

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.