BACKGROUND: Postoperative complications in colorectal surgery can significantly impact patient outcomes and healthcare costs. Accurate prediction of these complications enables targeted perioperative management, improving patient safety and optimizin...
Medical image reconstruction aims to generate high-quality images from incompletely sampled raw sensor data, which poses an ill-posed inverse problem. Traditional iterative reconstruction methods rely on prior information to empirically construct reg...
. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it has shortcomings including sensitivity to noise and rigidity in the value of the weights. Our goal wa...
Knowledge Distillation (KD) is one of the most effective and widely used methods for model compression of large models. It has achieved significant success with the meticulous development of distillation losses. However, most state-of-the-art KD loss...
Accurate traffic flow prediction is vital for intelligent transportation systems but presents significant challenges. Existing methods, however, have the following limitations: (1) insufficient exploration of interactions across different temporal sc...
Alzheimer's disease (AD) is a common type of dementia, with mild cognitive impairment (MCI) being a key precursor. Early MCI diagnosis is crucial for slowing AD progression, but distinguishing MCI from normal controls (NC) is challenging due to subtl...
Lung cancer is a major cause of cancer-related deaths, and early diagnosis and treatment are crucial for improving patients' survival outcomes. In this paper, we propose to employ convolutional neural networks to model the non-linear relationship bet...
The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit ...
In this study, we address the inherent challenges in radiotherapy (RT) plan quality assessment (QA). RT, a prevalent cancer treatment, utilizes high-energy beams to target tumors while sparing adjacent healthy tissues. Typically, an RT plan is refine...
The non-destructive, three-dimensional excitation-emission matrix (3D-EEM) based on fluorescence spectroscopy has been widely used in natural organic matter (NOM) monitoring in aquatic environments. However, the direct recognition of the species and ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.