Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation

Model-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation

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...

Semi-Supervised Thyroid Nodule Detection in Ultrasound Videos

Semi-Supervised Thyroid Nodule Detection in Ultrasound Videos

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...

Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation

Bilateral Supervision Network for Semi-Supervised Medical Image Segmentation

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 ...

Whole Reconstruction-Free System Design for Direct Positron Emission Imaging from Image Generation to Attenuation Correction

Whole Reconstruction-Free System Design for Direct Positron Emission Imaging from Image Generation to Attenuation Correction

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 the Functional Ultrasound Signal with GLM-Regularization

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...

AI-based Denoising of Head Impact Kinematics Measurements with Convolutional Neural Network for Traumatic Brain Injury Prediction

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...

Joint B0 and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning

Joint B0 and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning

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 ...

Heart Sound Abnormality Detection from Multi-Institutional Collaboration: Introducing a Federated Learning Framework

Heart Sound Abnormality Detection from Multi-Institutional Collaboration: Introducing a Federated Learning Framework

Academic Background Cardiovascular diseases (CVDs) have become one of the leading causes of death, particularly within the elderly population, making cardiovascular health a pressing societal concern. Early screening, diagnosis, and prognosis management are crucial for preventing hospitalizations. Heart sound signals carry rich physiological and pa...

Extracorporeal Closed-Loop Respiratory Regulation for Patients with Respiratory Difficulty Using a Soft Bionic Robot

Extracorporeal Closed-Loop Respiratory Regulation for Patients with Respiratory Difficulty Using a Soft Bionic Robot

Comprehensive Academic Report on a Scientific Paper In modern medicine, respiratory regulation is crucial for patients with respiratory dysfunction. However, currently used clinical positive pressure ventilators have issues such as long-term dependence and injury. While external auxiliary devices like the “iron lung” offer non-invasive alternatives...

A Double-Hurdle Quantification Model for Freezing of Gait of Parkinson’s Patients

Research on Quantitative Model for Freezing of Gait in Parkinson’s Patients Background Introduction Parkinson’s Disease (PD) is a common neurodegenerative disease, accompanied by complex motor disorders. In the later stages of Parkinson’s disease, the phenomenon of “Freezing of Gait” (FOG) becomes particularly prominent. FOG refers to a transient p...