Xiaodan Xing
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View article: A Pipeline for Automated Quality Control of Chest Radiographs
A Pipeline for Automated Quality Control of Chest Radiographs Open
This article presents a suite of quality control tools for chest radiographs based on traditional and artificial intelligence methods, developed and tested with data from 39 centers in seven countries.
View article: Enhancing Super-Resolution Network Efficacy in CT Imaging: Cost-Effective Simulation of Training Data
Enhancing Super-Resolution Network Efficacy in CT Imaging: Cost-Effective Simulation of Training Data Open
Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. However, procuring app…
View article: Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts
Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts Open
Radiology reporting generative AI holds significant potential to alleviate clinical workloads and streamline medical care. However, achieving high clinical accuracy is challenging, as radiological images often feature subtle lesions and in…
View article: Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language Conditioning
Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language Conditioning Open
Deep generative models have significantly advanced medical imaging analysis by enhancing dataset size and quality. Beyond mere data augmentation, our research in this paper highlights an additional, significant capacity of deep generative …
View article: Probing perfection: The relentless art of meddling for pulmonary airway segmentation from HRCT via a human-AI collaboration based active learning method
Probing perfection: The relentless art of meddling for pulmonary airway segmentation from HRCT via a human-AI collaboration based active learning method Open
In the realm of pulmonary tracheal segmentation, the scarcity of annotated data stands as a prevalent pain point in most medical segmentation endeavors. Concurrently, most Deep Learning (DL) methodologies employed in this domain invariably…
View article: Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method Open
In pulmonary tracheal segmentation, the scarcity of annotated data is a prevalent issue in medical segmentation. Additionally, Deep Learning (DL) methods face challenges: the opacity of 'black box' models and the need for performance enhan…
View article: Decoding Decision Reasoning: A Counterfactual-Powered Model for Knowledge Discovery
Decoding Decision Reasoning: A Counterfactual-Powered Model for Knowledge Discovery Open
In medical imaging, particularly in early disease detection and prognosis tasks, discerning the rationale behind an AI model's predictions is crucial for evaluating the reliability of its decisions. Conventional explanation methods face ch…
View article: Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness
Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness Open
In anticipation of potential future pandemics, we examined the challenges and opportunities presented by the COVID-19 outbreak. This analysis highlights how artificial intelligence (AI) and predictive models can support both patients and c…
View article: Post-COVID highlights: Challenges and solutions of artificial intelligence techniques for swift identification of COVID-19
Post-COVID highlights: Challenges and solutions of artificial intelligence techniques for swift identification of COVID-19 Open
Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rap…
View article: Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images
Assessing the Efficacy of Invisible Watermarks in AI-Generated Medical Images Open
AI-generated medical images are gaining growing popularity due to their potential to address the data scarcity challenge in the real world. However, the issue of accurate identification of these synthetic images, particularly when they exh…
View article: Dynamic Multimodal Information Bottleneck for Multimodality Classification
Dynamic Multimodal Information Bottleneck for Multimodality Classification Open
Effectively leveraging multimodal data such as various images, laboratory tests and clinical information is gaining traction in a variety of AI-based medical diagnosis and prognosis tasks. Most existing multi-modal techniques only focus on…
View article: Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation
Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation Open
This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model. The proposed pipeline aims to enhance diffusion tensor imaging (DTI) images by translating them into …
View article: Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19
Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19 Open
Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rap…
View article: SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis
SegmentAnything helps microscopy images based automatic and quantitative organoid detection and analysis Open
Organoids are self-organized 3D cell clusters that closely mimic the architecture and function of in vivo tissues and organs. Quantification of organoid morphology helps in studying organ development, drug discovery, and toxicity assessmen…
View article: Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach
Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach Open
A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocortico…
View article: Non-imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey
Non-imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey Open
Data quality is a key factor in the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can improve the accuracy, robustness, and privacy of downstream AI algorithms. However,…
View article: A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data
A pipeline to further enhance quality, integrity and reusability of the NCCID clinical data Open
The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the …
View article: Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation
Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation Open
Deep learning-based Generative Models have the potential to convert low-resolution CT images into high-resolution counterparts without long acquisition times and increased radiation exposure in thin-slice CT imaging. However, procuring app…
View article: You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images
You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images Open
Synthetic images generated from deep generative models have the potential to address data scarcity and data privacy issues. The selection of synthesis models is mostly based on image quality measurements, and most researchers favor synthet…
View article: The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus?
The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus? Open
Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world. Synthetic images generated from deep generative models can help alleviate the data scarcity problem, but…
View article: Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations Open
As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating corresponding segmentation masks for synthetic medical images is labo…
View article: Is Autoencoder Truly Applicable for 3D CT Super-Resolution?
Is Autoencoder Truly Applicable for 3D CT Super-Resolution? Open
Featured by a bottleneck structure, autoencoder (AE) and its variants have been largely applied in various medical image analysis tasks, such as segmentation, reconstruction and de-noising. Despite of their promising performances in aforem…
View article: Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey
Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey Open
Data quality is the key factor for the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can help improve the accuracy, robustness and privacy of downstream AI algorithms. H…
View article: Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation Open
Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers hav…
View article: Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI
Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI Open
Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently. Meanwhile, novel deep learning paradigms, e.g., Transformer based m…
View article: CS$^2$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention
CS$^2$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention Open
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such a problem of data and label scarcity, generative models hav…
View article: HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis Open
Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of…
View article: Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation
Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation Open
Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricular myocardial velocity mapping (3Dir MVM), which is a cardi…
View article: DS-GCNs: Connectome Classification using Dynamic Spectral Graph Convolution Networks with Assistant Task Training
DS-GCNs: Connectome Classification using Dynamic Spectral Graph Convolution Networks with Assistant Task Training Open
Functional connectivity (FC) matrices measure the regional interactions in the brain and have been widely used in neurological brain disease classification. A brain network, also named as connectome, could form a graph structure naturally,…