AIMC Topic: Neural Networks, Computer

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Multi-scale conv-attention U-Net for medical image segmentation.

Scientific reports
U-Net-based network structures are widely used in medical image segmentation. However, effectively capturing multi-scale features and spatial context information of complex organizational structures remains a challenge. To address this, we propose a ...

Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.

Medical science monitor : international medical journal of experimental and clinical research
Advancements in digital and precision medicine have fostered the rapid development of artificial intelligence (AI) applications, including machine learning, artificial neural networks (ANN), and deep learning, within the field of dentistry, particula...

Sub-diffuse Reflectance Spectroscopy Combined With Machine Learning Method for Oral Mucosal Disease Identification.

Lasers in surgery and medicine
OBJECTIVES: Oral squamous cell carcinoma (OSCC) is the sixth-highest incidence of malignant tumors worldwide. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Here, we prese...

Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.

PloS one
Protein-ligand interactions are crucial in drug discovery. Accurately predicting protein-ligand binding affinity is essential for screening potential drugs. Graph neural networks have proven highly effective in modeling spatial relationships and thre...

Event-based optical flow on neuromorphic processor: ANN vs. SNN comparison based on activation sparsification.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) for event-based optical flow are claimed to be computationally more efficient than their artificial neural networks (ANNs) counterparts, but a fair comparison is missing in the literature. In this work, we propose an ev...

A prompt regularization approach to enhance few-shot class-incremental learning with Two-Stage Classifier.

Neural networks : the official journal of the International Neural Network Society
With a limited number of labeled samples, Few-Shot Class-Incremental Learning (FSCIL) seeks to efficiently train and update models without forgetting previously learned tasks. Because pre-trained models can learn extensive feature representations fro...

A neural network approach to glomerular filtration rate estimation: a single-centre retrospective audit.

Nuclear medicine communications
OBJECTIVES: The 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation without race correction factor is frequently used for an estimate of glomerular filtration rate (eGFR) and to support a single-sample GFR regime. This study exa...

Speech emotion recognition with light weight deep neural ensemble model using hand crafted features.

Scientific reports
Automatic emotion detection has become crucial in various domains, such as healthcare, neuroscience, smart home technologies, and human-computer interaction (HCI). Speech Emotion Recognition (SER) has attracted considerable attention because of its p...

Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.

Scientific reports
Wheat is one of the world's most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential f...

Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.

Scientific reports
Advancements in medical technology have extended long-term electrocardiogram (ECG) monitoring from the traditional 24 h to 7-14 days, significantly enriching ECG data. However, this poses unprecedented challenges for physicians in analyzing these ext...