Deep Image Understanding Using Multilayered Contexts

Deep Retinal Image Understanding

This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in other fields of computer vision such as object detection and image classification, and we bring their power to the study of eye fundus...

Image Understanding with Deep Architectures

Cystoscopy Image Classication Using Deep Convolutional Neural Networks

In the past three decades, the use of smart methods in medical diagnostic systems has attractedthe attention of many researchers. However, no smart activity has been provided in the eld ofmedical image processing for diagnosis of bladder cancer through cystoscopy images despite the highprevalence in the world. In this paper, two well-known convolutional neural networks (CNNs) ...

Understanding Depression across Cultural Contexts

Yi, a young U.S.-born Hmong woman, is seriously distressed. She is having difficulty sleeping; she has lost her appetite; and she lacks interest in her studies. Yi also says that she has little energy and is having a difficult time balancing school and family obligations. Although she does not spontaneously use emotional terms to describe how she feels, when asked she agrees that she is unhappy...

A Deep Learning Pipeline for Image Understanding and Acoustic Modeling

One of the biggest challenges artificial intelligence faces is making sense of the real world through sensory signals such as audio or video. Noisy inputs, varying object viewpoints, deformations and lighting conditions turn it into a high-dimensional problem which cannot be efficiently solved without learning from data. This thesis explores a general way of learning from high dimensional data ...