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Xue-Cheng Tai

Chief Scientist

NORCE Norwegian Research Centre

Norway

Research Interests

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About

Dr. Xue-Cheng Tai is the Chief Scientist at the Norwegian Sequencing Centre in Norway. His research focuses on advanced methodologies in image segmentation and reconstruction, including the application of deep convolutional neural networks, variational models, and convex optimization techniques. His recent publications also explore computational methods for blood flow simulations and image enhancement using physics-informed neural networks.

Articles (20)

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2025

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2022

Collaborators (1)

Qi Wang

University of South Carolina

UNITED STATES

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Xue-Cheng Tai

Research Interests

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About

Articles (20)

Deep convolutional neural networks meet variational shape compactness priors for image segmentation

SUPERIORIZED ITERATION ALGORITHM FOR CT IMAGE SIMULTANEOUS RECONSTRUCTION AND SEGMENTATION

A Fast Minimization Algorithm for the Euler Elastica Model Based on a Bilinear Decomposition

A MATHEMATICAL EXPLANATION OF UNET

A MESHLESS SOLVER FOR BLOOD FLOW SIMULATIONS IN ELASTIC VESSELS USING A PHYSICS-INFORMED NEURAL NETWORK

A Priori Error Estimate of Deep Mixed Residual Method for Elliptic PDEs

A Variational Model for Nonuniform Low-Light Image Enhancement

A new method to compute the blood flow equations using the physics-informed neural operator

DOUBLE-WELL NET FOR IMAGE SEGMENTATION

Efficient Convex Optimization for Non-convex Non-smooth Image Restoration

A Binary Characterization Method for Shape Convexity and Applications

An explainable artificial intelligence-based typification of chronic inflammatory responses enhances glioma prognosis

Elastica Models for Color Image Regularization

Geodesic Models With Convexity Shape Prior

Redefined chronic inflammatory responses as prognostic markers in glioma: an explainable artificial intelligence model

A Fast Operator-splitting Method for Beltrami Color Image Denoising

A new binary representation method for shape convexity and application to image segmentation

AN OPERATOR-SPLITTING METHOD FOR THE GAUSSIAN CURVATURE REGULARIZATION MODEL WITH APPLICATIONS TO SURFACE SMOOTHING AND IMAGING

Convex Object(s) Characterization and Segmentation Using Level Set Function

Deep Convolutional Neural Networks with Spatial Regularization, Volume and Star-Shape Priors for Image Segmentation

Collaborators (1)

Qi Wang

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