Development and Validation of a Deep Learning Radiomics Model Combined with Clinical Radiological Features for Predicting Occult Peritoneal Metastasis in Patients with Pancreatic Ductal Adenocarcinoma Background Pancreatic ductal adenocarcinoma (PDAC) is an extremely lethal malignancy with a 5-year survival rate of approximately 11%. The poor progn...

Research on Using Deep Learning to Combine Mammography and Ultrasound Images for Predicting Malignancy of BI-RADS US 4A Lesions in Women with Dense Breasts Background Breast cancer is the most common malignant tumor in women, with a relatively high incidence and mortality rate. Previous studies have found that women with dense breasts are more like...

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...