The Chinese University of Hong Kong

The Hong Kong Research Institute of Textiles and Apparel Limited

Xi Wang

Xiao-ming Tao

Pheng Ann Heng

Fangyao Tang

Su Liu

Xia Guo

Shi-rui Liu

Bao Yang

Yim Lui Carol Cheung

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 an intelligent bionic human body part model detection device and a method for manufacturing same. The device comprises: a bionic human body part model (); and multiple optical fiber grating sensing units () which are integrated on an optical fibre and arranged at multiple pre-determined positions of the bionic human body part model (). The device can improve the accuracy of the detection of pressure applied to the intelligent bionic human body part model.

Intelligent bionic human body part model detection device and method for manufacturing same

Growing community of inventors

Hong Kong, China

Xi Wang