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Xin Wang

Researcher at University at Buffalo

Publications -  56
Citations -  2433

Xin Wang is an academic researcher from University at Buffalo. The author has contributed to research in topics: Computer science & Sentence. The author has an hindex of 14, co-authored 46 publications receiving 1444 citations. Previous affiliations of Xin Wang include University at Albany, SUNY & Heilongjiang University.

Papers
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Journal ArticleDOI

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.

TL;DR: A deep learning model was developed to extract visual features from volumetric chest CT scans for the detection of coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions.
Journal ArticleDOI

Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network.

TL;DR: The proposed CNN-RNN deep learning framework was able to accurately detect ICH and its subtypes with fast speed, suggesting its potential for assisting radiologists and physicians in their clinical diagnosis workflow.
Book ChapterDOI

Cancer Metastasis Detection via Spatially Structured Deep Network

TL;DR: A novel deep neural network, namely Spatially Structured Network (Spatio-Net) is proposed to tackle the metastasis detection problem in WSIs by integrating the Convolutional Neural Network with the 2D Long-Short Term Memory (2D-LSTM).
Proceedings Article

Chinese Sentence-Level Sentiment Classification Based on Fuzzy Sets

TL;DR: This paper proposes a fine-to-coarse strategy to estimate sentence sentiment intensity and builds membership functions to indicate the degrees of an opinionated sentence in different fuzzy sets, and determines sentence-level polarity under maximum membership principle.
Journal ArticleDOI

Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging.

TL;DR: This study proposes a novel deep neural network solution (TreeVes-Net) that allows machines to perceive FFR values directly from static coronary CT angiography images and demonstrates the effectiveness of the framework and its superiority to seven FFR computation methods based on machine learning.