A5056204100- Radiomics and Machine Learning in Medical Imaging
- Medical Image Segmentation Techniques
- Medical Imaging Techniques and Applications
- Image and Signal Denoising Methods
- Sparse and Compressive Sensing Techniques
- Computer Graphics and Visualization Techniques
- Advanced Image Processing Techniques
- Advanced MRI Techniques and Applications
- Lung Cancer Diagnosis and Treatment
- MRI in cancer diagnosis
- Advanced X-ray and CT Imaging
- AI in cancer detection
- Digital Imaging for Blood Diseases
- Generative Adversarial Networks and Image Synthesis
- Medical Imaging and Analysis
- Advanced Neuroimaging Techniques and Applications
Nanchang University
2025
Henan University
2022-2024
Shenzhen Institutes of Advanced Technology
2022-2024
Chinese Academy of Sciences
2022
Accurate segmentation of lung nodules is crucial for the early diagnosis and treatment cancer in clinical practice. However, similarity between surrounding tissues has made their a longstanding challenge.
Abstract Objective. Magnetic resonance imaging (MRI) is critical in medical diagnosis and treatment by capturing detailed features, such as subtle tissue changes, which help clinicians make precise diagnoses. However, the widely used single diffusion model has limitations accurately more complex details. This study aims to address these proposing an efficient method enhance reconstruction of features MRI. Approach. We present a detail-preserving that leverages multiple models (DP-MDM)...
Detail features of magnetic resonance images play a cru-cial role in accurate medical diagnosis and treatment, as they capture subtle changes that pose challenges for doc-tors when performing precise judgments. However, the widely utilized naive diffusion model has limitations, it fails to accurately more intricate details. To en-hance quality MRI reconstruction, we propose comprehensive detail-preserving reconstruction method using multiple models extract structure detail k-space domain...
Abstract Objective. Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, high-density distribution of nuclei, cell adhesion. Approach. In paper, we present an interactive nuclei framework that increases precision segmentation. Our incorporates expert monitoring gather much prior information possible segment nucleus images through limited...
Computed Tomography (CT) technology reduces radiation haz-ards to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. Score-based generative models are widely used in sparse-view CT re-construction, performance diminishes significantly with a sharp reduction projection angles. Therefore, we propose an ultra-sparse view reconstruction method utilizing multi-scale dif-fusion (MSDiff), designed concentrate on global distribution of...
Image segmentation is a complex and core technique in the medical image domain. However, low-quality images, such as images with weak edges, may bring considerable challenges for radiologists. In this paper, we propose an adaptive weighted curvature-based active contour model by coupling heat kernel convolution adaptively high-order total variation to improve diagnosis effectiveness. The numerical experimental results on 3T/5T MRI datasets demonstrate that proposed quite efficient robust...
Image segmentation is an essential step for many applications in the field of image analysis.One main challenges this task how to accurately locate complicated boundary and properly segment a region interest efficiently.To end, paper provides new scheme by combining adaptive weight function high-order total variation term improve robustness classical active contour model.In order reduce computational complexity, our model uses heat kernel convolution with approximate perimeter area.Due...