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James Z. Wang

Researcher at Pennsylvania State University

Publications -  234
Citations -  23185

James Z. Wang is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Image retrieval & Automatic image annotation. The author has an hindex of 57, co-authored 225 publications receiving 21890 citations. Previous affiliations of James Z. Wang include Penn State College of Information Sciences and Technology & University of Minnesota.

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Proceedings ArticleDOI

An investigation into three visual characteristics of complex scenes that evoke human emotion

TL;DR: The experimental results show that color features related most strongly with the positivity of perceived emotions, the texture features related more to calmness or excitement, and roundness, angularity, and simplicity related similarly with both of these emotions dimensions.
Proceedings ArticleDOI

In-vivo imaging of nanoshell extravasation from solid tumor vasculature by photoacoustic microscopy

TL;DR: Experimental results show that nanoshell accumulation is heterogeneous in tumors: more concentrated within the tumor cortex and largely absent from the tumor core, which correlates with others' observation that drug delivery within tumor cores is ineffective because of both high interstitial pressure and tendency to necrosis of tumor cores.
Proceedings ArticleDOI

Towards efficient automated characterization of irregular histology images via transformation to frieze-like patterns

TL;DR: It is shown that the reduced dimensionality of the patterns may allow them to be characterized with greater efficiency and accuracy than by previous methods of image analysis, which in turn enables potentially greater accuracy in the retrieval of histology images exhibiting abnormalities of interest to pathologists and researchers.
Book ChapterDOI

LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-Weighted MR Images.

TL;DR: LambdaUNet as mentioned in this paper extends UNet by replacing convolutional layers with Lambda+ layers, which transform both intra-slice and inter-slice context around a pixel into linear functions, called lambdas, which are then applied to the pixel to produce informative 2.5D features.