The Chinese University of Hong Kong

Pheng Ann Heng

Lin Shi

Defeng Wang

Xi Wang

Yim Lui Carol Cheung

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.

Multi-task deep-learning method and system to classify diabetic macular edema for different optical coherence tomography devices

Disclosed are methods, systems and apparatuses for detection of arterial input function (AIF) in MRI, specially DCE MR images, comprising automatic selection of AIF based on affinity propagation (AP) clustering method.

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Hong Kong, China

Pheng Ann Heng