BACKGROUND: Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fib...
PURPOSE: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital patholog...
This study focuses on improving coordination among teams of heterogeneous robots, including unmanned aerial vehicles and unmanned ground vehicles, drawing inspiration from natural pack-hunting strategies. The goal is to increase the effectiveness of ...
In recent decades, medical image registration technology has undergone significant development, becoming one of the core technologies in medical image analysis. With the rise of deep learning, deep learning-based medical image registration methods ha...
Vehicle-mounted flexible robotic arms (VFRAs) are crucial in enhancing operational capabilities in sectors where human intervention is limited due to accessibility or safety concerns, such as hazardous environments or precision surgery. This paper in...
Although prediction models for heart transplantation outcomes have been developed previously, a comprehensive benchmarking of survival machine learning methods for mortality prognosis in the most contemporary era of heart transplants following the 20...
Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum fr...
BACKGROUND: Ventilator-associated pneumonia (VAP) is a common nosocomial infection in ICU, significantly associated with poor outcomes. However, there is currently a lack of reliable and interpretable tools for assessing the risk of in-hospital morta...
Adversarial training has become a primary method for enhancing the robustness of deep learning models. In recent years, fast adversarial training methods have gained widespread attention due to their lower computational cost. However, since fast adve...
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