论文标题
眼底图像中的自动病变细分和病理近视分类
Automatic lesion segmentation and Pathological Myopia classification in fundus images
论文作者
论文摘要
在本文中，我们介绍了诊断病理近视（PM）的算法，并检测视网膜结构和病变，例如高椎间盘（OD），中央凹，萎缩和分离。所有这些任务均在PM患者的眼底成像中执行，这是参加病理近视挑战（Palm）的要求。挑战是为半天挑战组织的，这是IEEE国际生物医学成像研讨会的卫星事件。转移学习均在所有任务中使用Xecceion作为基线模型。同样，在光盘分割算法管道中使用了Yolo架构的一些关键思想。我们已经根据AUC-ROC，F1得分，平均骰子得分和平均欧几里得距离评估了模型的绩效。对于初始活动，我们的方法显示出令人满意的结果。
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.
