【醫學+深度論文：F31】2017 IOS Press Automatic Identification of Glaucoma Using Deep Learning Methods

31

2017 IOS Press

Automatic Identification of Glaucoma Using Deep Learning Methods

Method ： 分類
Dataset：HRF , RIM-ONE r1,RIM-ONE r2,RIM-ONE r3
Architecture： ROI + GoogLeNet
Results: AC HRF 90%, RIM-ONE r1 94.2% ,RIM-ONE r2 86.2% ,RIM-ONE r3 86.4% 總 87.6%

眼底圖像缺陷：very subtle lesions, poor image quality, and illumination problems

The purpose of our work : to perform this detection even in images with

• low contrast
• high amount of noise
• low resolution
• ROI barely visible

Common practice :

• post-processing
• remove blood vessels

Aim

• robust network

Method

• find ROI

GoogLeNet 將圖像分類爲 ROI和背景
開發了一種利用滑動窗口掃描 以搜索該區域的算法

• data augmentation

adding deformations and noise

• clasification
  GoogLeNet

Disscussion

• 先提取 ROI ，數據增強 ，分類 （沒什麼新思想）
• 擴大數據
• 圖像預處理