AIMC Topic: Neural Networks, Computer

Clear Filters Showing 5471 to 5480 of 31376 articles

Breast histopathological imaging using ultra-fast fluorescence confocal microscopy to identify cancer lesions at early stage.

Microscopy research and technique
Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography a...

Novel use of deep neural networks on photographic identification of epaulette sharks (Hemiscyllium ocellatum) across life stages.

Journal of fish biology
Photographic identification (photo ID) is an established method that is used to count animals and track individuals' movements. This method performs well with some species of elasmobranchs (i.e., sharks, skates, and rays) where individuals have disti...

Data-free knowledge distillation via generator-free data generation for Non-IID federated learning.

Neural networks : the official journal of the International Neural Network Society
Data heterogeneity (Non-IID) on Federated Learning (FL) is currently a widely publicized problem, which leads to local model drift and performance degradation. Because of the advantage of knowledge distillation, it has been explored in some recent wo...

Improving accuracy and efficiency of the machined PEEK denture based on NSGA-II integrated GABP neural network.

Dental materials : official publication of the Academy of Dental Materials
OBJECTIVES: The polymer polyetheretherketone (PEEK) is gradually being used in dental restorations because of its excellent mechanical properties, chemical resistance, fatigue resistance, thermal stability, radiation translucency and good biocompatib...

Diversity matters: Cross-head mutual mean-teaching for semi-supervised medical image segmentation.

Medical image analysis
Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Nevertheless, existing state-of-the-art (SOTA) methods encounter challenges in accurately predic...

Neuro-XAI: Explainable deep learning framework based on deeplabV3+ and bayesian optimization for segmentation and classification of brain tumor in MRI scans.

Journal of neuroscience methods
The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it t...

Role of Natural Language Processing in Automatic Detection of Unexpected Findings in Radiology Reports: A Comparative Study of RoBERTa, CNN, and ChatGPT.

Academic radiology
RATIONALE AND OBJECTIVES: Large Language Models can capture the context of radiological reports, offering high accuracy in detecting unexpected findings. We aim to fine-tune a Robustly Optimized BERTĀ PretrainingĀ Approach (RoBERTa) model for the autom...

MFCC-CNN: A patient-independent seizure prediction model.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: Automatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) signals in different patients limits the use of prediction models in clinical applications.

Towards consensual representation: Model-agnostic knowledge extraction for dual heterogeneous federated fault diagnosis.

Neural networks : the official journal of the International Neural Network Society
Federated fault diagnosis has attracted increasing attention in industrial cloud-edge collaboration scenarios, where a ubiquitous assumption is that client models have the same architecture. Practically, this assumption cannot always be fulfilled due...

Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.

Journal of medical imaging and radiation oncology
INTRODUCTION: Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolu...