Yongyi Shi
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View article: Xray2Xray: World Model from Chest X-rays with Volumetric Context
Xray2Xray: World Model from Chest X-rays with Volumetric Context Open
Chest X-rays (CXRs) are the most widely used medical imaging modality and play a pivotal role in diagnosing diseases. However, as 2D projection images, CXRs are limited by structural superposition, which constrains their effectiveness in p…
View article: Manifold Topological Deep Learning for Biomedical Data
Manifold Topological Deep Learning for Biomedical Data Open
View article: Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing Open
Leveraging multi-center data for medical analytics presents challenges due to privacy concerns and data heterogeneity. While distributed approaches such as federated learning has gained traction, they remain vulnerable to privacy breaches,…
View article: Manifold Topological Deep Learning for Biomedical Data
Manifold Topological Deep Learning for Biomedical Data Open
Recently, topological deep learning (TDL), which integrates algebraic topology with deep neural networks, has achieved tremendous success in processing point-cloud data, emerging as a promising paradigm in data science. However, TDL has no…
View article: Low-dose computed tomography perceptual image quality assessment
Low-dose computed tomography perceptual image quality assessment Open
In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered…
View article: CT-based Anomaly Detection of Liver Tumors Using Generative Diffusion Prior
CT-based Anomaly Detection of Liver Tumors Using Generative Diffusion Prior Open
CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify pathological structures. However, these methods may produce…
View article: Blind CT Image Quality Assessment Using DDPM-Derived Content and Transformer-Based Evaluator
Blind CT Image Quality Assessment Using DDPM-Derived Content and Transformer-Based Evaluator Open
Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evalu…
View article: Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction
Learned Tensor Neural Network Texture Prior for Photon-Counting CT Reconstruction Open
Photon-counting computed tomography (PCCT) reconstructs multiple energy-channel images to describe the same object, where there exists a strong correlation among different channel images. In addition, reconstruction of each channel image s…
View article: Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT
Diffusion Prior Regularized Iterative Reconstruction for Low-dose CT Open
Computed tomography (CT) involves a patient's exposure to ionizing radiation. To reduce the radiation dose, we can either lower the X-ray photon count or down-sample projection views. However, either of the ways often compromises image qua…
View article: Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator
Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator Open
Lowering radiation dose per view and utilizing sparse views per scan are two common CT scan modes, albeit often leading to distorted images characterized by noise and streak artifacts. Blind image quality assessment (BIQA) strives to evalu…
View article: A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT
A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT Open
Most penalized maximum likelihood methods for tomographic image reconstruction based on Bayes’ law include a freely adjustable hyperparameter to balance the data fidelity term and the prior/penalty term for a specific noise–resolution trad…
View article: Enabling Competitive Performance of Medical Imaging with Diffusion Model-generated Images without Privacy Leakage
Enabling Competitive Performance of Medical Imaging with Diffusion Model-generated Images without Privacy Leakage Open
Deep learning methods have impacted almost every research field, demonstrating notable successes in medical imaging tasks such as denoising and super-resolution. However, the prerequisite for deep learning is data at scale, but data sharin…
View article: Task-based Assessment of Deep Networks for Sinogram Denoising with A Transformer-based Observer
Task-based Assessment of Deep Networks for Sinogram Denoising with A Transformer-based Observer Open
A variety of supervise learning methods are available for low-dose CT denoising in the sinogram domain. Traditional model observers are widely employed to evaluate these methods. However, the sinogram domain evaluation remains an open chal…
View article: Lesion classification by model-based feature extraction: A differential affine invariant model of soft tissue elasticity
Lesion classification by model-based feature extraction: A differential affine invariant model of soft tissue elasticity Open
The elasticity of soft tissues has been widely considered as a characteristic property to differentiate between healthy and vicious tissues and, therefore, motivated several elasticity imaging modalities, such as Ultrasound Elastography, M…
View article: Clinical Characteristics and Outcomes of 217 Kidney Transplantation Recipients Hospitalized with COVID-19: A Systematic Review
Clinical Characteristics and Outcomes of 217 Kidney Transplantation Recipients Hospitalized with COVID-19: A Systematic Review Open
Immunosuppressed kidney transplant recipients may have increased risk of causing severe disease during hospitalization of COVID-19.We conducted this review for better understanding the clinical characteristics and outcomes of this populati…
View article: Spectral CT Reconstruction via Low-Rank Representation and Region-Specific Texture Preserving Markov Random Field Regularization
Spectral CT Reconstruction via Low-Rank Representation and Region-Specific Texture Preserving Markov Random Field Regularization Open
Photon-counting spectral computed tomography (CT) is capable of material characterization and can improve diagnostic performance over traditional clinical CT. However, it suffers from photon count starving for each individual energy channe…
View article: Constructing a tissue-specific texture prior by machine learning from previous full-dose scan for Bayesian reconstruction of current ultralow-dose CT images
Constructing a tissue-specific texture prior by machine learning from previous full-dose scan for Bayesian reconstruction of current ultralow-dose CT images Open
Purpose: Bayesian theory provides a sound framework for ultralow-dose computed tomography (ULdCT) image reconstruction with two terms for modeling the data statistical property and incorporating a priori knowledge for the ima…
View article: Energy enhanced tissue texture in spectral computed tomography for lesion classification
Energy enhanced tissue texture in spectral computed tomography for lesion classification Open
Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels, i.e., the tissue heterogeneity, and has been recognized as important biomarkers in various clinical tasks. Spectral computed tomography (CT) is belie…
View article: Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss
Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss Open
The continuous development and extensive use of computed tomography (CT) in medical practice has raised a public concern over the associated radiation dose to the patient. Reducing the radiation dose may lead to increased noise and artifac…
View article: Cyber-Physical System Security and Protection Strategies for the Smart Substation
Cyber-Physical System Security and Protection Strategies for the Smart Substation Open
As an important part of the smart grid, smart substation is the key operating parameters collection point and control execution point, thus its safe and stable operation is one of the fundamental bases of ensuring the grid continues to pro…