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Huan Wang

Researcher at Northeastern University (China)

Publications -  17
Citations -  160

Huan Wang is an academic researcher from Northeastern University (China). The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 3, co-authored 8 publications receiving 90 citations.

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On domain modelling of the service system with its application to enterprise information systems

TL;DR: A domain modelling framework for the service system is proposed and its application to the enterprise information system is outlined and the FCBPSS is applied to both infrastructure and substance systems, which is novel and effective to modelling of service systems including enterprise information systems.
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X-Net: Multi-branch UNet-like network for liver and tumor segmentation from 3D abdominal CT scans

TL;DR: Experimental results on the MICCAI 2017 Liver Tumor Segmentation Challenge dataset and 3DIRCADb dataset have demonstrated that the proposed method can provide superior performance to the state-of-the-art methods with respect to the certain benchmarks for liver and tumor segmentation.
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Predatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems

TL;DR: The result of the test shows that the proposed approach PSS-PSO is superior to all the seven algorithms, and takes the particle swarm optimization (PSO) algorithm as the local optimizer.
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DCLNet: Dual Closed-loop Networks for face super-resolution

TL;DR: This work represents the first attempt to introduce multiple dual learning networks into face super-resolution model to constrain the possible mapping space and presents a progressive facial prior estimation framework and a new prior-guided feature enhancement module to integrate the facial prior knowledge and guide the face image super- resolution.
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A Novel Local Human Visual Perceptual Texture Description with Key Feature Selection for Texture Classification

TL;DR: A PCA-based feature selection method exploiting the structure of the principal components of the feature set to find a subset of the original feature vector, where the features reflect the most representative characteristics for the textures in the given image dataset.