Potential of Self-Distilling Masked Image Transformer in Longitudinal MRI - Automatic Segmentation of Cervical Lymph Node Metastases Report Introduction In tumor radiotherapy, automatic segmentation technology promises to improve speed and reduce inter-reader variability caused by manual segmentation. In radiotherapy clinical practice, accurate and...
Protein Structure Prediction: Challenges, Progress, and Shifts in Research Paradigms Protein structure prediction is an important interdisciplinary research topic that has attracted researchers from various fields including biochemistry, medicine, physics, mathematics, and computer science. Researchers have adopted multiple research paradigms to so...
Introduction Fall events are common across various clinical populations, with usual risk assessments including visual observation of individual gait. However, gait observation assessments are typically confined to laboratory settings, involving standardized walking protocol tests to identify potential defects that might increase fall risk. Subtle d...
Breakthrough in Automated Echocardiogram Diagnosis via Deep Learning: A Comparative Study of Self-Supervised Learning Methods Research Background With the rapid development of artificial intelligence and machine learning technologies, their role in medical imaging diagnosis is becoming increasingly significant. In particular, Self-Supervised Learni...
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...
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 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: 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 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: 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), ...