Prospective Longitudinal Analysis of Physiologic MRI-Based Tumor Habitat Predicts Short-Term Patient Outcomes in IDH-Wildtype Glioblastoma Academic Background Glioblastoma (GBM) is a highly malignant brain tumor characterized by significant intratumoral heterogeneity, which is evident not only in gene expression and histopathology but also in macro...
ABVS Breast Tumor Segmentation Research Based on CNN and Dilated Sampling Self-Attention Academic Background Breast cancer is the second most common cancer worldwide, and early and accurate detection is crucial for improving patient prognosis and reducing mortality. Although various imaging techniques (such as X-ray mammography, magnetic resonance ...
Academic Background and Problem Introduction In the field of education today, Public Speaking Anxiety (PSA) is a widespread phenomenon, especially among non-native language learners. This anxiety not only affects learners’ ability to express themselves but may also hinder their personal development. To help learners overcome this issue, researchers...
Academic Background In the fields of machine learning and neural networks, continual learning is an important research direction. The goal of continual learning is to enable models to continuously learn new knowledge across a series of tasks while avoiding forgetting previously acquired knowledge. However, existing continual learning methods face a...
Theory and Application of Complex Quantized Minimum Error Entropy with Fiducial Points: Breakthroughs in Model Regression Academic Background In the fields of machine learning and signal processing, the presence of non-Gaussian noise often adversely affects model performance. Although the traditional Mean Squared Error (MSE) is theoretically and co...
New Breakthrough in Multi-Label Image Classification: Dual Relation Transformer Network Academic Background Multi-Label Image Classification (MLIC) is a fundamental yet highly challenging problem in the field of computer vision. Unlike single-label image classification, MLIC aims to assign multiple labels to objects within a single image. Due to th...
Adaptive Distributed Multi-Task Learning Framework ADAMT: Efficient Image Recognition in Mobile Ad-hoc Networks Academic Background Distributed machine learning in Mobile Ad-hoc Networks (MANETs) faces significant challenges. These challenges primarily stem from the limited computational resources of devices, non-independent and identically distrib...
Academic Background Deep Reinforcement Learning (DRL) has achieved remarkable success in various fields such as gaming, robotics, navigation, computer vision, and finance. However, existing DRL algorithms generally suffer from low sample efficiency, requiring vast amounts of data and training steps to achieve desired performance. Particularly in co...
Research on Abnormal Behavior Detection in Surveillance Video Based on Probabilistic Memory Auto-Encoding Network Academic Background In intelligent surveillance systems, abnormal behavior detection is a crucial function widely applied in anti-terrorism, social stability maintenance, and public safety assurance. However, a core challenge in abnorma...
Research on Weight Initialization Method for Restricted Boltzmann Machines Based on Statistical Mechanical Analysis Academic Background In deep learning, the initialization of neural network weights significantly impacts the training effectiveness of models. Particularly in feed-forward neural networks, several dataset-free weight initialization me...