Dinggang Shen
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View article: Deciphering age- and sex-specific patterns of coronary artery atherosclerosis from a large Chinese cohort
Deciphering age- and sex-specific patterns of coronary artery atherosclerosis from a large Chinese cohort Open
Coronary artery disease poses a significant public health threat, and coronary computed tomography angiography is the preferred imaging modality for diagnosis and risk assessment of coronary artery disease through plaque evaluation. Howeve…
View article: A novel AI-powered radiographic analysis surpasses specialists in stage II–IV periodontitis detection: a multicenter diagnostic study
A novel AI-powered radiographic analysis surpasses specialists in stage II–IV periodontitis detection: a multicenter diagnostic study Open
Missed periodontitis diagnoses are common, and AI dental radiography systems based on clinical standards can enhance reliable detection. We introduce and evaluate HC-Net+, a deep-learning model that mimics clinical pathways while integrati…
View article: Enhancing SNR of CEST MRI Through Noise‐to‐Noise Deep Learning With K‐Space Data Consistency
Enhancing SNR of CEST MRI Through Noise‐to‐Noise Deep Learning With K‐Space Data Consistency Open
Tha aim of this study is to develop a deep learning‐based retrospective denoising method, via noise‐to‐noise (N2N) CEST, to enhance the SNR of acquired CEST images. The N2NCEST model was trained using pairs of noisy CEST images through a N…
View article: Brain Connectivity Network Structure Learning For Brain Disorder Diagnosis
Brain Connectivity Network Structure Learning For Brain Disorder Diagnosis Open
Recent studies in neuroscience highlight the significant potential of brain connectivity networks, which are commonly constructed from functional magnetic resonance imaging (fMRI) data for brain disorder diagnosis. Traditional brain connec…
View article: Correction to “Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning” [Jul 16 1505-1516]
Correction to “Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning” [Jul 16 1505-1516] Open
Correction to “Scalable High-Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning”
View article: Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training
Adapting Foundation Model for Dental Caries Detection with Dual-View Co-Training Open
Accurate dental caries detection from panoramic X-rays plays a pivotal role in preventing lesion progression. However, current detection methods often yield suboptimal accuracy due to subtle contrast variations and diverse lesion morpholog…
View article: Deep-Learning-Based Multi-Modal Fusion for Fast MR Reconstruction
Deep-Learning-Based Multi-Modal Fusion for Fast MR Reconstruction Open
T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired magnetic resonance (MR) modalities that can provide complementary information for clinical and research usages. However, the relatively long acquisition t…
View article: Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network
Enhancement of Perivascular Spaces Using Densely Connected Deep Convolutional Neural Network Open
Perivascular spaces (PVS) in the human brain are related to various brain diseases. However, it is difficult to quantify them due to their thin and blurry appearance. In this paper, we introduce a deep-learning-based method, which can enha…
View article: Submillimeter MR fingerprinting using deep learning–based tissue quantification
Submillimeter MR fingerprinting using deep learning–based tissue quantification Open
PURPOSE: To develop a rapid 2D MR fingerprinting technique with a submillimeter in-plane resolution using a deep learning-based tissue quantification approach. METHODS: A rapid and high-resolution MR fingerprinting technique was developed …
View article: Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores
Weakly Supervised Deep Learning for Brain Disease Prognosis Using MRI and Incomplete Clinical Scores Open
As a hot topic in brain disease prognosis, predicting clinical measures of subjects based on brain magnetic resonance imaging (MRI) data helps to assess the stage of pathology and predict future development of the disease. Due to incomplet…
View article: Hippocampus Radiomic Biomarkers for the Diagnosis of Amnestic Mild Cognitive Impairment: A Machine Learning Method
Hippocampus Radiomic Biomarkers for the Diagnosis of Amnestic Mild Cognitive Impairment: A Machine Learning Method Open
Background: Recent evidence suggests the presence of hippocampal neuroanatomical abnormalities in subjects of amnestic mild cognitive impairment (aMCI). Our study aimed to identify the radiomic biomarkers of the hippocampus for building th…
View article: Erratum to Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting
Erratum to Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting Open
Erratum to "Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting"
View article: Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting
Deep Learning for Fast and Spatially Constrained Tissue Quantification From Highly Accelerated Data in Magnetic Resonance Fingerprinting Open
Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of human body. Although MRF has demonstrated improved scan efficiency as compared to conventio…
View article: Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients
Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients Open
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to …
View article: Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches
Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches Open
Biomedical imaging Is an important source of information in cancer research. Characterizations of cancer morphology at onset, progression, and in response to treatment provide complementary information to that gleaned from genomics and cli…
View article: An adaptive AI-based virtual reality sports system for adolescents with excess body weight: a randomized controlled trial
An adaptive AI-based virtual reality sports system for adolescents with excess body weight: a randomized controlled trial Open
Adolescents with obesity face numerous health risks and encounter barriers that lead to physical inactivity. We developed a virtual reality sports system, named REVERIE (Real-World Exercise and VR-Based Exercise Research in Education), whi…
View article: PerioAI: A digital system for periodontal disease diagnosis from an intra-oral scan and cone-beam CT image
PerioAI: A digital system for periodontal disease diagnosis from an intra-oral scan and cone-beam CT image Open
Periodontal disease diagnosis and treatment planning are critical for preventing bone and tooth loss. Clinically, dentists manually measure periodontal pocket depth with probes while integrating bone structure from imaging to assess period…
View article: Diffusion Neuroimaging of Speech Acquisition in Infants
Diffusion Neuroimaging of Speech Acquisition in Infants Open
How white matter maturation supports speech — an emergent property of integrated human brain networks — remains unclear. Leveraging the Baby Connectome Project, the largest longitudinal infant neuroimaging and behavioral dataset available,…
View article: Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks
Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks Open
Purpose We developed an artificial intelligence system (AIS) using multi-view multi-level convolutional neural networks for breast cancer detection, diagnosis, and BI-RADS categorization support in mammography. Methods Twenty-four thousand…
View article: Radiology-GPT: A large language model for radiology
Radiology-GPT: A large language model for radiology Open
We introduce Radiology-GPT, a large language model for radiology. Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstrates superior performance compared to general language mode…
View article: UniCAD: Efficient and Extendable Architecture for Multi-Task Computer-Aided Diagnosis System
UniCAD: Efficient and Extendable Architecture for Multi-Task Computer-Aided Diagnosis System Open
The growing complexity and scale of visual model pre-training have made developing and deploying multi-task computer-aided diagnosis (CAD) systems increasingly challenging and resource-intensive. Furthermore, the medical imaging community …
View article: Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans
Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans Open
Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more impor…
View article: Ethics of Foundation Models in Computational Pathology: Overview of Contemporary Issues and Future Implications
Ethics of Foundation Models in Computational Pathology: Overview of Contemporary Issues and Future Implications Open
Artificial intelligence (AI) has profoundly transformed our lives, reshaping industries and impacting nearly every aspect of society over the past few decades. It has recently become even more influential, primarily due to the rise of foun…
View article: Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data
Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data Open
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Thanks to the valuable insights into the brain's anatom…