The Chinese University of Hong Kong

Yim Lui Carol Cheung

Pheng Ann Heng

Xi Wang

Fangyao Tang

A deep-learning method and systems for analyzing optical coherence tomography (OCT) images based on a convolutional neural network are provided. The method includes extracting a feature from one or more three-dimensional OCT volumetric scan images and classifying the OCT images with respect to diabetic macular edema (DME) based on results the feature extracted. The step of extracting a feature from one or more three-dimensional OCT volumetric scan images is performed by a neural network such as a neural network based on a ResNet-34 architecture. The method can further include extracting a feature from one or more two-dimensional (2D) OCT B-scan images and classifying the OCT images with respect to DME based on results of the 2D feature extracted. The step of extracting a feature from one or more 2D OCT B-scan images is performed by a neural network such as a neural network based on a ResNet-18 architecture.

Growing community of inventors

Hong Kong, China

Yim Lui Carol Cheung