Abstract：
:Automatic and accurate classification of retinal optical coherence tomography (OCT) images is essential to assist ophthalmologist in the diagnosis and grading of macular diseases. Clinically, ophthalmologists usually diagnose macular diseases according to the structures of macular lesions, whose morphologies, size, and numbers are important criteria. In this paper, we propose a novel lesion-aware convolutional neural network (LACNN) method for retinal OCT image classification, in which retinal lesions within OCT images are utilized to guide the CNN to achieve more accurate classification. The LACNN simulates the ophthalmologists' diagnosis that focuses on local lesion-related regions when analyzing the OCT image. Specifically, we first design a lesion detection network to generate a soft attention map from the whole OCT image. The attention map is then incorporated into a classification network to weight the contributions of local convolutional representations. Guided by the lesion attention map, the classification network can utilize the information from local lesion-related regions to further accelerate the network training process and improve the OCT classification. Our experimental results on two clinically acquired OCT datasets demonstrate the effectiveness and efficiency of the proposed LACNN method for retinal OCT image classification.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Fang L,Wang C,Li S,Rabbani H,Chen X,Liu Zdoi
10.1109/TMI.2019.2898414subject
Has Abstractpub_date
2019-08-01 00:00:00pages
1959-1970issue
8eissn
0278-0062issn
1558-254Xjournal_volume
38pub_type
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