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Kai-Lung Hua

Researcher at National Taiwan University of Science and Technology

Publications -  159
Citations -  2124

Kai-Lung Hua is an academic researcher from National Taiwan University of Science and Technology. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 18, co-authored 142 publications receiving 1467 citations. Previous affiliations of Kai-Lung Hua include Mackay Memorial Hospital & Purdue University.

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Journal ArticleDOI

Computer-aided classification of lung nodules on computed tomography images via deep learning technique.

TL;DR: This study attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques and introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images.
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Intelligent deployment of UAVs in 5G heterogeneous communication environment for improved coverage

TL;DR: The proposed approach utilizes the priority-wise dominance and the entropy approaches for providing solutions to the two problems considered in this paper, namely, Macro Base Station (MBS) decision problem and the cooperative UAV allocation problem.
Journal ArticleDOI

DeepDemosaicking: Adaptive Image Demosaicking via Multiple Deep Fully Convolutional Networks.

TL;DR: The proposed method for image demosaicking based on deep convolutional neural networks outperforms several existing and state-of-the-art methods in terms of both the subjective and objective evaluations.
Proceedings ArticleDOI

What Dress Fits Me Best?: Fashion Recommendation on the Clothing Style for Personal Body Shape

TL;DR: The experimental results demonstrate the superiority of the first framework for learning the compatibility of clothing styles and body shapes from social big data, with the goal to recommend a user about what to wear better in relation to his/her essential body attributes.
Proceedings ArticleDOI

What are the Fashion Trends in New York

TL;DR: A novel algorithm is presented that automatically discovers visual style elements representing fashion trends for a certain season based on the stylistic coherent and unique characteristics of catwalk show videos.