Ge Wang

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Clark & Crossan Endowed Chair Professor

Rensselaer Polytechnic Institute

United States

Research Interests

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About

Ge Wang is the Clark & Crossan Endowed Chair Professor at Rensselaer Polytechnic Institute in the United States. His research interests include metal artifact reduction, advanced models for cardiac computed tomography, and the development of artificial neural networks. Recent articles authored by Professor Wang focus on topics such as low-dose CT image denoising and multimodal foundation models for lung cancer screening.

Recent Grants

Grant: Close

Photon-counting X-ray and Optical Tomography for Preclinical Cancer Research

Open Date: 2019-09-12

Close Date: 2024-08-31

Grant: Close

Acquisition of MARS photon-counting micro-CT scanner

Open Date: 2019-08-01

Close Date: 2020-07-31

Grant: Close

High dose efficiency CT System

Open Date: 2014-09-17

Close Date: 2019-11-30

Grant: Close

Grating-based X-ray Phase-contrast Tomography Methods

Open Date: 2014-04-01

Close Date: 2019-03-31

Grant: Close

Acquisition of a 500nm Resolution Nano-CT Scanner

Open Date: 2008-12-15

Close Date: 2009-12-14

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Assistant Professor

Atrium Health Wake Forest Baptist

UNITED STATES

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Ge Wang

Research Interests

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About

Recent Grants

Articles (30)

Medical multimodal multitask foundation model for lung cancer screening

Metal artifact reduction and contrast agent performance as a function of source spectral shape

From seeing X-ray sparks to sparking neural circuits

Physics-Informed Score-Based Diffusion Model for Limited-Angle Reconstruction of Cardiac Computed Tomography

Dimensionality and dynamics for next-generation artificial neural networks

PFCM: Poisson Flow Consistency Models for Low-Dose CT Image Denoising

Guided synthesis of annotated lung CT images with pathologies using a multi-conditioned denoising diffusion probabilistic model (mDDPM)

Editorial Flagship Toward the Future

Patch-based dual-domain photon-counting CT data correction with residual-based WGAN-ViT

Syn2Real: synthesis of CT image ring artifacts for deep learning-based correction

Towards the Generation of Medical Imaging Classifiers Robust to Common Perturbations

PPFM: Image Denoising in Photon-Counting CT Using Single-Step Posterior Sampling Poisson Flow Generative Models

Contrast-enhanced photon-counting micro-CT of tumor xenograft models

Simulated deep CT characterization of liver metastases with high-resolution filtered back projection reconstruction

Hybrid U-Net and Swin-transformer network for limited-angle cardiac computed tomography

Diving into deep learning

LIT-Former: Linking In-Plane and Through-Plane Transformers for Simultaneous CT Image Denoising and Deblurring

Quad-Net: Quad-Domain Network for CT Metal Artifact Reduction

Wavelet-Improved Score-Based Generative Model for Medical Imaging

CT Image Denoising and Deblurring With Deep Learning: Current Status and Perspectives

Multimodal Radiology AI

QS-ADN: quasi-supervised artifact disentanglement network for low-dose CT image denoising by local similarity among unpaired data

Fluorescence molecular tomography for quantum yield and lifetime

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

DishBrain plays Pong and promises more

Open-Source Inline Generative AI for Fast Cardiac MRI

Noise Suppression With Similarity-Based Self-Supervised Deep Learning

Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey

Preliminary landscape analysis of deep tomographic imaging patents

Focus on machine learning models in medical imaging

Collaborators (5)

Mannudeep K Kalra

Klaus Mueller

Juergen Hahn

Pingkun Yan

Qing Lyu

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