Multi-template Meta-information Regularized Network for Alzheimer’s Disease Diagnosis: A Study Based on Structural MRI Research Background Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder, and its diagnosis and early detection is a significant challenge in the medical field. Structural MRI (sMRI) is widely used in computer-aided...
Application of Implicit Regularization Learning Network Based on Noise Generation and Imaging Mechanisms in Low-Dose CT Reconstruction Low-Dose Computed Tomography (LDCT) has become an important tool in modern medical imaging, aiming to reduce radiation risks while maintaining image quality. However, reducing the X-ray dose often leads to data corr...
Unsupervised Fusion of Unaligned PAT and MRI Images Using Mutually Enhancing Cross-Modality Image Generation and Registration Methods Background and Research Objectives In recent years, photoacoustic tomography (PAT) and magnetic resonance imaging (MRI) have been widely used in preclinical research as cutting-edge biomedical imaging techniques. PAT...
Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation Background Introduction Medical image segmentation plays a crucial role in clinical diagnosis as it helps doctors identify and analyze diseases. However, this task typically faces challenges such as sensitive data, privacy issues, and expensive annotation costs. W...
Research Report on Semi-Supervised Detection of Thyroid Nodules in Ultrasound Videos Research Background Thyroid nodules are common thyroid diseases. Early screening and diagnosis of thyroid nodules typically rely on ultrasound examinations, a common non-invasive detection method used for detecting various diseases such as thyroid nodules, breast c...
Research Background and Motivation Medical image segmentation is of great significance in the image analysis of anatomical structures and lesion areas, as well as in clinical diagnosis. However, existing fully supervised learning methods rely on a large amount of annotated data, and obtaining pixel-level annotated data for medical images is costly ...
Background Introduction A hundred years ago, Hevesy first proposed using radioactive tracers as biological markers in plants, later validated through experiments in rats. This discovery propelled the development of nuclear medicine and molecular imaging in the biomedical field, making it possible to quantitatively visualize biological processes at ...
Evoked Component Analysis (ECA): Decomposing Functional Ultrasound Signals Based on GLM Regularization Background The analysis of functional neuroimaging data aims to uncover spatial and temporal patterns of brain activity. Existing data analysis methods mainly fall into two categories: fully data-driven analysis methods and methods that rely on pr...
Research and Application of Denoising Head Impact Kinematics Measurement Based on Convolutional Neural Networks Research Background Mild Traumatic Brain Injury (MTBI) is a global health threat. Humans often face the risk of MTBI in situations such as falls, traffic accidents, and sports. According to statistics, there were over 27 million brain inj...
Low-Field MRI Image Reconstruction Using Physics-Informed Deep Learning Background: The application of magnetic resonance imaging (MRI) technology in low-field magnetic resonance imaging has gained increasing attention in recent years. Low-field MRI, due to its low cost and simplified maintenance, is considered to have a broad application prospect ...