Xiaokun Liang
YOU?
Author Swipe
View article: A Study of a Skew-Hadamard URA Coded Neutron PGAI Technique with a Rotating Modulation Collimator
A Study of a Skew-Hadamard URA Coded Neutron PGAI Technique with a Rotating Modulation Collimator Open
Prompt Gamma Activation Imaging (PGAI) is an advanced elemental mapping technique that probes elemental distributions within a sample by detecting characteristic gamma rays emitted under neutron irradiation. Conventional PGAI relies on hig…
View article: Development of deep learning-based narrow-band imaging endocytoscopic classification for predicting colorectal lesions from a retrospective study
Development of deep learning-based narrow-band imaging endocytoscopic classification for predicting colorectal lesions from a retrospective study Open
Data-driven approaches have advanced colorectal lesion diagnosis in digestive endoscopy, yet their application in endocytoscopy (EC)-a high-magnification imaging technique-remains limited, with most studies relying on conventional machine …
View article: Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results
Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results Open
View article: Multimodal deep learning model for prognostic prediction in cervical cancer receiving definitive radiotherapy: a multi-center study
Multimodal deep learning model for prognostic prediction in cervical cancer receiving definitive radiotherapy: a multi-center study Open
View article: Advances and Challenges in Respiratory Sound Analysis: A Technique Review Based on the ICBHI2017 Database
Advances and Challenges in Respiratory Sound Analysis: A Technique Review Based on the ICBHI2017 Database Open
Respiratory diseases present significant global health challenges. Recent advances in respiratory sound analysis (RSA) have shown great potential for automated disease diagnosis and patient management. The International Conference on Biome…
View article: Advances and Challenges in Respiratory Sound Analysis: A Technique Review Based on the ICBHI2017 Database
Advances and Challenges in Respiratory Sound Analysis: A Technique Review Based on the ICBHI2017 Database Open
Respiratory diseases present significant global health challenges. Recent advances in respiratory sound analysis (RSA) have shown great potential for automated disease diagnosis and patient management. The International Conference on Biome…
View article: Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge
Benchmark of Segmentation Techniques for Pelvic Fracture in CT and X-ray: Summary of the PENGWIN 2024 Challenge Open
The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and efficiently delineating the bone fragments remains a significant …
View article: C2FAU-Net: A Deep Learning Approach with Multi-scale Strategy for Automated Delineation of Organs-at-risk in Cervical Cancer High-dose Rate Brachytherapy
C2FAU-Net: A Deep Learning Approach with Multi-scale Strategy for Automated Delineation of Organs-at-risk in Cervical Cancer High-dose Rate Brachytherapy Open
Background and Purpose: Precise delineation of pelvic organs-at-risk (OARs) is crucial for high-dose-rate brachytherapy (HDR-BT) in cervical cancer treatment. While deep learning methods have shown promise in automatic delineation, substan…
View article: Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge
Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge Open
Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic seg…
View article: Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models
Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models Open
Cone-Beam Computed Tomography (CBCT) holds significant clinical value in image-guided radiotherapy (IGRT). However, CBCT images of low-density soft tissues are often plagued with artifacts and noise, which can lead to missed diagnoses and …
View article: Deep learning models for CT image classification: a comprehensive literature review
Deep learning models for CT image classification: a comprehensive literature review Open
This review underscores the pivotal role of DL in advancing CT image analysis, particularly for COVID-19 and lung nodule detection. The integration of DL models into clinical workflows shows promising potential to enhance diagnostic accura…
View article: Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy
Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy Open
Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless…
View article: Diffusion probabilistic priors for zero‐shot low‐dose CT image denoising
Diffusion probabilistic priors for zero‐shot low‐dose CT image denoising Open
Background Denoising low‐dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning‐based approaches have made significant advancements in this area in recent years. However, these methods …
View article: Mammo-Clustering: A Multi-views Tri-level Information Fusion Context Clustering Framework for Localization and Classification in Mammography
Mammo-Clustering: A Multi-views Tri-level Information Fusion Context Clustering Framework for Localization and Classification in Mammography Open
Breast cancer is a significant global health issue, and the diagnosis of breast imaging has always been challenging. Mammography images typically have extremely high resolution, with lesions occupying only a very small area. Down-sampling …
View article: Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet/CinC Challenge 2016 Database
Review of Phonocardiogram Signal Analysis: Insights from the PhysioNet/CinC Challenge 2016 Database Open
The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring, accurate diagnosis, and efficient management of cardiovascular diseases. It has the potential to revolutionize cardiovascular care and improve pati…
View article: COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning
COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning Open
The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose p…
View article: Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study
Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study Open
Background Despite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim of th…
View article: Volumetric feature points integration with bio-structure-informed guidance for deformable multi-modal CT image registration
Volumetric feature points integration with bio-structure-informed guidance for deformable multi-modal CT image registration Open
Objective. Medical image registration represents a fundamental challenge in medical image processing. Specifically, CT-CBCT registration has significant implications in the context of image-guided radiation therapy (IGRT). However, traditi…
View article: Three-Dimensional Medical Image Fusion with Deformable Cross-Attention
Three-Dimensional Medical Image Fusion with Deformable Cross-Attention Open
Multimodal medical image fusion plays an instrumental role in several areas of medical image processing, particularly in disease recognition and tumor detection. Traditional fusion methods tend to process each modality independently before…
View article: QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration
QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration Open
Rigid pre-registration involving local-global matching or other large deformation scenarios is crucial. Current popular methods rely on unsupervised learning based on grayscale similarity, but under circumstances where different poses lead…
View article: Ring artifacts correction for computed tomography image using unsupervised contrastive learning
Ring artifacts correction for computed tomography image using unsupervised contrastive learning Open
Objective. Computed tomography (CT) is a widely employed imaging technology for disease detection. However, CT images often suffer from ring artifacts, which may result from hardware defects and other factors. These artifacts compromise im…
View article: XGenRecon: A New Perspective in Ultra-Sparse CBCT Reconstruction through Geometry-Controlled X-ray Projection Generation
XGenRecon: A New Perspective in Ultra-Sparse CBCT Reconstruction through Geometry-Controlled X-ray Projection Generation Open
We propose a novel paradigm for cone-beam computed tomography (CBCT) reconstruction from ultra-sparse X-ray projections, by introducing a framework that generates auxiliary X-ray projections under controlled geometric parameters. This inno…
View article: XGenRecon: A New Perspective in Ultra-Sparse CBCT Reconstruction through Geometry-Controlled X-ray Projection Generation
XGenRecon: A New Perspective in Ultra-Sparse CBCT Reconstruction through Geometry-Controlled X-ray Projection Generation Open
We propose a novel paradigm for cone-beam computed tomography (CBCT) reconstruction from ultra-sparse X-ray projections, by introducing a framework that generates auxiliary X-ray projections under controlled geometric parameters. This inno…
View article: Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains
Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains Open
During the process of computed tomography (CT), metallic implants often cause disruptive artifacts in the reconstructed images, impeding accurate diagnosis. Several supervised deep learning-based approaches have been proposed for reducing …
View article: INR-LDDMM: Fluid-based Medical Image Registration Integrating Implicit Neural Representation and Large Deformation Diffeomorphic Metric Mapping
INR-LDDMM: Fluid-based Medical Image Registration Integrating Implicit Neural Representation and Large Deformation Diffeomorphic Metric Mapping Open
We propose a fluid-based registration framework of medical images based on implicit neural representation. By integrating implicit neural representation and Large Deformable Diffeomorphic Metric Mapping (LDDMM), we employ a Multilayer Perc…
View article: Noninvasive imaging evaluation of peritoneal recurrence and chemotherapy benefit in gastric cancer after gastrectomy：a multicenter study
Noninvasive imaging evaluation of peritoneal recurrence and chemotherapy benefit in gastric cancer after gastrectomy：a multicenter study Open
The noninvasive and explainable model developed from preoperative CT images could accurately predict PR and chemotherapy benefit in patients with GC, which will allow the optimization of individual decision-making.
View article: XTransCT: Ultra-Fast Volumetric CT Reconstruction using Two Orthogonal X-Ray Projections for Image-guided Radiation Therapy via a Transformer Network
XTransCT: Ultra-Fast Volumetric CT Reconstruction using Two Orthogonal X-Ray Projections for Image-guided Radiation Therapy via a Transformer Network Open
Computed tomography (CT) scans offer a detailed, three-dimensional representation of patients' internal organs. However, conventional CT reconstruction techniques necessitate acquiring hundreds or thousands of x-ray projections through a c…
View article: Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising Open
Denoising low-dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning-based approaches have made significant advancements in this area in recent years. However, these methods typically r…
View article: Accurate breast cancer diagnosis using a stable feature ranking algorithm
Accurate breast cancer diagnosis using a stable feature ranking algorithm Open
View article: Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT
Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT Open
Objective To develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image r…