Investigating Useful Features for Overall Survival Prediction in Patients with Low-Grade Glioma Using Histology Slides

Useful Features for Overall Survival Prediction in Low-Grade Glioma Patients Academic Background Glioma is a type of neoplastic growth in the brain that usually poses a serious threat to the patients’ lives. In most cases, glioma eventually leads to the death of the patient. The analysis of glioma typically involves examining pathological slices of...

Improving the Segmentation of Pediatric Low-Grade Gliomas through Multitask Learning

Improved Segmentation of Pediatric Low-Grade Gliomas Through Multitask Learning Background Introduction The segmentation of pediatric brain tumors is a critical task in tumor volume analysis and artificial intelligence algorithms. However, this process is time-consuming and requires the expertise of neuroradiologists. Although significant research ...

Deep-Learning-Based Motor Imagery EEG Classification by Exploiting the Functional Connectivity of Cortical Source Imaging

Deep-learning-based Motor Imagery EEG Classification by Exploiting the Functional Connectivity of Cortical Source Imaging Research Background and Motivation A brain-computer interface (BCI) is a system that directly decodes and outputs brain activity information without relying on related neural pathways and muscles, thereby achieving communication...

AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment Enabled by Large Language Models

AutoAlign: A Fully Automated and Efficient Knowledge Graph Alignment Method Driven by Large Language Models Knowledge Graphs (KG) have been widely applied in fields such as question-answering systems, dialogue systems, and recommendation systems. However, different Knowledge Graphs often store the same real-world entities in various forms, leading ...

Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing

Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing

Deep Graph Memory Network for Forgetting-Robust Knowledge Tracing In recent years, Knowledge Tracing (KT) has attracted widespread attention as an important method for personalized learning. The goal of KT is to predict the accuracy of a student’s answers to new questions by utilizing their past answer history to estimate their knowledge state. How...

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

DeepSleepNet: An Automatic Sleep Stage Scoring Model Based on Single-Channel EEG Background Introduction Sleep has a significant impact on human health, and monitoring sleep quality is crucial in medical research and practice. Typically, sleep experts score sleep stages by analyzing various physiological signals such as electroencephalogram (EEG), ...

Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification

In recent years, due to the high incidence and lethality of brain tumors, rapid and accurate detection and classification of brain tumors have become particularly important. Brain tumors include both malignant and non-malignant types, and their abnormal growth can cause long-term damage to the brain. Magnetic Resonance Imaging (MRI) is a commonly u...

Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics from Surface EMG

Musculoskeletal models have been widely used in biomechanical analysis because they can estimate motion variables that are difficult to measure directly in living organisms, such as muscle forces and joint moments. Traditional physics-driven computational musculoskeletal models can explain the dynamic interactions between neural inputs to muscles, ...

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG

In the current educational environment, understanding students’ learning styles is crucial for improving their learning efficiency. Specifically, the identification of visual learning styles can help teachers and students adopt more effective strategies in the teaching and learning process. Currently, automatic identification of visual learning sty...

Development and Validation of a Deep Learning Radiomics Model with Clinical-Radiological Characteristics for the Identification of Occult Peritoneal Metastases in Patients with Pancreatic Ductal Adenocarcinoma

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...