Background and Significance Object re-identification (Re-ID) is an essential task in computer vision aimed at identifying specific objects across different times and scenes. Driven by deep learning, particularly convolutional neural networks (CNNs), this field has made significant strides. However, the emergence of vision transformers has opened ne...
Deep Learning Model Reveals Mechanisms of Semantic Satiation Semantic satiation, the phenomenon where a word or phrase loses its meaning after being repeated many times, is a well-known psychological phenomenon. However, the micro-neural computational principles underlying this mechanism remain unknown. This paper uses a continuous coupled neural n...
EHR-HGCN: A Novel Hybrid Heterogeneous Graph Convolutional Network Method for Electronic Health Record Text Classification Academic Background With the rapid development of Natural Language Processing (NLP), text classification has become an important research direction in this field. Text classification not only helps us understand the knowledge b...
Classification of 3D MRI Gliomas and Alzheimer’s Disease Based on the Siamese-Transport Domain Adaptation Framework Background In computer-aided diagnosis, 3D magnetic resonance imaging (MRI) screening plays a vital role in the early diagnosis of various brain diseases, effectively preventing the deterioration of the condition. Glioma is a common m...
The IEEE “Transactions on Neural Systems and Rehabilitation Engineering” published a paper titled “Sleep Stage Classification Using Attention-Based Deep Learning for Single-Channel EEG” in Volume 29, 2021. The author of the article include Emadeldeen Edele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. The main go...
GCTNet: Graph Convolution Transformer Network for Detecting Major Depressive Disorder Based on EEG Signals Research Background Major Depressive Disorder (MDD) is a prevalent mental illness characterized by significant and persistent low mood, affecting over 350 million people worldwide. MDD is one of the leading causes of suicide, resulting in appr...
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...
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...
Development and Validation of Large-Scale Machine Learning Algorithms for Cardiovascular Diagnosis Based on Electrocardiograms Introduction Cardiovascular diseases (CV) have long been a major source of global disease burden. Early diagnosis and intervention are crucial for reducing complications, healthcare utilization, and associated costs. Tradit...