AIMC Topic: Neural Networks, Computer

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Global-Local Transformer Network for Automatic Retinal Pathological Fluid Segmentation in Optical Coherence Tomography Images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: As a pivotal biomarker, the accurate segmentation of retinal pathological fluid such as intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED), was a critical task for diagnosis and treatme...

Reinforcement learning using neural networks in estimating an optimal dynamic treatment regime in patients with sepsis.

Computer methods and programs in biomedicine
OBJECTIVE: Early fluid resuscitation is crucial in the treatment of sepsis, yet the optimal dosage remains debated. This study aims to determine the optimal multi-stage fluid resuscitation dosage for sepsis patients.

A hybrid PKI and spiking neural network approach for enhancing security and energy efficiency in IoMT-based healthcare 5.0.

SLAS technology
In the rapidly evolving field of healthcare 5.0, the Internet of Medical Things (IoMT) is expected to be an enabler that allows smart medical devices to collaborate and communicate with healthcare networks to speed up procedures, enhance care, and im...

Artificial intelligence medical image-aided diagnosis system for risk assessment of adjacent segment degeneration after lumbar fusion surgery.

SLAS technology
The existing assessment of adjacent segment degeneration (ASD) risk after lumbar fusion surgery focuses on a single type of clinical information or imaging manifestations. In the early stages, it is difficult to show obvious degeneration characterist...

A novel deep neural network approach to detect and monitor cocaine drug abuse.

Computers in biology and medicine
PURPOSE: Cocaine is one of the most commonly used drugs that may lead to physical and mental health problems. It is necessary to identify individuals having cocaine use disorder as early as possible to monitor them properly. The objective of this wor...

Mitosis detection and classification for breast cancer diagnosis: What we know and what is next.

Computers in biology and medicine
Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine...

EmT: A Novel Transformer for Generalized Cross-Subject EEG Emotion Recognition.

IEEE transactions on neural networks and learning systems
Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous techniques emphasize learning spatial and short-term temporal patterns, there has been a limited emphasis on ...

Comparison of deep learning models for facial attractiveness assessment on 3D photos.

Journal of dentistry
OBJECTIVES: Convolutional neural networks (CNNs) have demonstrated remarkable success in orthodontics. This study aimed to evaluate the accuracy and precision of several prominent CNN models for evaluating the facial attractiveness in Chinese orthodo...

Boosting polyp screening with improved point-teacher weakly semi-supervised.

Computers in biology and medicine
Polyps, like a silent time bomb in the gut, are always lurking and can explode into deadly colorectal cancer at any time. Many methods are attempted to maximize the early detection of colon polyps by screening, however, there are still face some chal...

Generative adversarial networks in medical image reconstruction: A systematic literature review.

Computers in biology and medicine
PURPOSE: Recent advancements in generative adversarial networks (GANs) have demonstrated substantial potential in medical image processing. Despite this progress, reconstructing images from incomplete data remains a challenge, impacting image quality...