Paper Info
Title
Retinal Microaneurysm Detection Using Clinical Report Guided Multi-sieving CNN.
Abstract
Timely detection and treatment of microaneurysms (MA) is a critical step to prevent the development of vision-threatening eye diseases such as diabetic retinopathy. However, detecting MAs in fundus images is a highly challenging task due to the large variation of imaging conditions. In this paper, we focus on developing an interleaved deep mining technique to cope intelligently with the unbalanced MA detection problem. Specifically, we present a clinical report guided multi-sieving convolutional neural network (MS-CNN) which leverages a small amount of supervised information in clinical reports to identify the potential MA regions via a text-to-image mapping in the feature space. These potential MA regions are then interleaved with the fundus image information for multi-sieving deep mining in a highly unbalanced classification problem. Critically, the clinical reports are employed to bridge the semantic gap between low-level image features and high-level diagnostic information. Extensive evaluations show our framework achieves 99.7% precision and 87.8% recall, comparing favorably with the state-of-the-art algorithms. Integration of expert domain knowledge and image information demonstrates the feasibility to reduce the training difficulty of the classifiers under extremely unbalanced data distribution.
Year
Venue
Field
2017
MICCAI
Computer vision,Feature vector,Pattern recognition,Domain knowledge,Feature (computer vision),Computer science,Convolutional neural network,Semantic gap,Fundus (eye),Artificial intelligence,Retinal microaneurysm,Fundus image
DocType
Citations 
PageRank 
Conference
2
0.43
References 
Authors
8
7
Name
Order
Citations
PageRank
Ling Dai191.69
bin sheng292.34
Qiang Wu320.43
Huating Li4262.01
Xuhong Hou5474.03
Weiping Jia6293.74
Ruogu Fang728721.78