Transforming the Operating Room into a Mixed Reality Environment: A Prospective Clinical Study on Aneurysm Clipping The surgical treatment of cerebral aneurysms is a highly complex and delicate process in neurosurgery. Researchers continue to explore new technologies and methods to improve surgical outcomes. In recent years, the development of Mixe...
Multi-center Study on Artificial Intelligence-based Classification of Breast Lesions In the field of breast cancer, early diagnosis is crucial for improving treatment efficacy and survival rate. Breast cancer can be mainly divided into two categories: in situ carcinoma and invasive carcinoma, which have significant differences in treatment strategi...
Application of Radiomics in Predicting Local Control in Postoperative Stereotactic Radiotherapy for Brain Metastasis Patients Academic Background Brain Metastases (BMs) are the most common malignant brain tumors, far surpassing primary brain tumors like gliomas in incidence. Recent medical guidelines recommend surgical treatment for patients with s...
Study on Metabolic Differences between IDH Mutant and Wild-type Glioma Cells Using Raman Spectroscopy and Machine Learning Platform Background Introduction In the diagnosis and treatment of gliomas, formalin-fixed, paraffin-embedded (FFPE) tissue sections are commonly used. However, due to background noise interference from the embedding medium, th...
Enhancing Super-Resolution Imaging and Volume Electron Microscopy with Deep Learning Algorithms Based on Diffusion Models Background Introduction Electron Microscopy (EM) as a high-resolution imaging tool has made significant breakthroughs in cell biology. Traditional EM techniques are primarily used for two-dimensional imaging, and although they h...
Automated Spinal Cord Segmentation Pipeline Based on Convolutional Neural Network (CNN) for Individualized Cat Spinal Cord Stimulation Modeling Background and Research Motivation Spinal cord stimulation (SCS) is a widely used treatment method for chronic pain management. In recent years, it has also been used to modulate neural activity, aiming to ...
Application of Attention-guided Graph Structure Learning Network for EEG-enabled Auditory Attention Detection Academic Background The “cocktail party effect” describes the human brain’s ability to selectively concentrate attention on one speaker while ignoring others in a multi-talker environment. However, for individuals with hearing impairments, ...
Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding Background Introduction Electroencephalogram (EEG) signals are widely used in brain-computer interface (BCI) tasks due to their non-invasive nature, portability, and low acquisition cost. However, EEG signals suffer from low signal-to-noise ratio, sensitivity to electr...
Background Introduction The study of Event-Related Potentials (ERPs) provides important information about brain mechanisms, particularly in elucidating various psychological processes. In these studies, multi-channel electroencephalograms (EEGs) are typically recorded while subjects perform specific tasks, and the trials are categorized based on st...
Layer-Dependent Effects of Chronic Neural Electrode Implants on Neural Functional Connectivity in Mice Introduction This study explores the long-term effects of chronically implanted microelectrodes on neural functional connectivity within the brains of C57BL6 wild-type mice. Implanted intracerebral electrodes enable the recording and electrical st...