A Magnified Adaptive Feature Pyramid Network for automatic microaneurysms detection.
A Magnified Adaptive Feature Pyramid Network for automatic microaneurysms detection.
复制标题
用于自动微动脉瘤检测的放大自适应特征金字塔网络。
DOI：
10.1016/j.compbiomed.2021.105000
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Lv Ruichan
中科院分区：
文献类型：
--
作者：
Sun Song;Cao Zhicheng;Liao Dingying;Lv Ruichan
Diabetic retinopathy (DR), as an important complication of diabetes, is the primary cause of blindness in adults. Automatic DR detection poses a challenge which is crucial for early DR screening. Currently, the vast majority of DR is diagnosed through fundus images, where the microaneurysm (MA) has been widely used as the most distinguishable marker. Research works on automatic DR detection have traditionally utilized manually designed operators, while a few recent researchers have explored deep learning techniques for this topic. But due to issues such as the extremely small size of microaneurysms, low resolution of fundus pictures, and insufficient imaging depth, the DR detection problem is quite challenging and remains unsolved. To address these issues, this research proposes a new deep learning model (Magnified Adaptive Feature Pyramid Network, MAFP-Net) for DR detection, which conducts super-resolution on low quality fundus images and integrates an improved feature pyramid structure while utilizing a standard two-stage detection network as the backbone. Our proposed detection model needs no pre-segmented patches to train the CNN network. When tested on the E-ophtha-MA dataset, the sensitivity value of our method reached as high as 83.5% at false positives per image (FPI) of 8 and the F1 value achieved 0.676, exceeding all those of the state-of-the-art algorithms as well as the human performance of experienced physicians. Similar results were achieved on another public dataset of IDRiD.
