Radiomics-based Prediction of Local Control in Patients with Brain Metastases Following Postoperative Stereotactic Radiotherapy

Information Science, Artificial Intelligence, Medicine, Neurology, Neurosurgery, Medical Imaging, Oncology,
radiomics   machine learning   local failure prediction   brain metastases   artificial intelligence  

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...

Raman-Based Machine Learning Platform Reveals Unique Metabolic Differences Between IDHmut and IDHwt Glioma

Information Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology, Medical Imaging, Oncology, Life Sciences, Cell Biology, Biochemistry,
Raman spectroscopy   glioma   machine learning   FFPE tissue   lipid metabolism  

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...

Diffusion-based Deep Learning Method for Augmenting Ultrastructural Imaging and Volume Electron Microscopy

Diffusion-based Deep Learning Method for Augmenting Ultrastructural Imaging and Volume Electron Microscopy

Information Science, Electronic Information, Artificial Intelligence, Medicine, Medical Imaging, Life Sciences, Cell Biology, Biochemistry, Biophysics,
diffusion model   electron microscopy   volume electron microscopy   high-resolution imaging   deep learning  

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...

A Novel CNN-Based Image Segmentation Pipeline for Individualized Feline Spinal Cord Stimulation Modeling

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Bioengineering,
spinal cord stimulation   machine learning   automatic segmentation   axonal recruitment   convolutional neural network   artificial intelligence  

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 ...

Attention-Guided Graph Structure Learning Network for EEG-enabled Auditory Attention Detection

Attention-Guided Graph Structure Learning Network for EEG-enabled Auditory Attention Detection

Engineering, Electrical Engineering, Information Science, Electronic Information, Computer Science, Artificial Intelligence, Medicine, Neurology,
auditory attention detection   electroencephalography   graph structure learning   graph convolutional network   neuro-steered hearing aids  

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, ...

A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology,
brain-computer interfaces   deep learning   euclidean alignment   EEG decoding   transfer learning  

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...