AIMC Topic: Convolutional Neural Networks

Clear Filters Showing 221 to 230 of 291 articles

Enabling scale and rotation invariance in convolutional neural networks with retina like transformation.

Neural networks : the official journal of the International Neural Network Society
Traditional convolutional neural networks (CNNs) struggle with scale and rotation transformations, resulting in reduced performance on transformed images. Previous research focused on designing specific CNN modules to extract transformation-invariant...

SHFormer: Dynamic spectral filtering convolutional neural network and high-pass kernel generation transformer for adaptive MRI reconstruction.

Neural networks : the official journal of the International Neural Network Society
Attention Mechanism (AM) selectively focuses on essential information for imaging tasks and captures relationships between regions from distant pixel neighborhoods to compute feature representations. Accelerated magnetic resonance image (MRI) reconst...

Two-stage convolutional neural network for segmentation and detection of carotid web on CT angiography.

Journal of neurointerventional surgery
BACKGROUND: Carotid web (CaW) is a risk factor for ischemic stroke, mainly in young patients with stroke of undetermined etiology. Its detection is challenging, especially among non-experienced physicians.

f-statistics-based ancestry profiling and convolutional neural network phenotyping shed new light on the structure of genetic and spike shape diversity in Aegilops tauschii Coss.

Proceedings of the Japan Academy. Series B, Physical and biological sciences
Aegilops tauschii Coss., a progenitor of bread wheat, is an important wild genetic resource for breeding. The species comprises three genetically defined lineages (TauL1, TauL2, and TauL3), each displaying valuable phenotypes in agronomic traits, inc...

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data.

Journal of chemical information and modeling
Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors fo...

Classification of sounds from Pacific white-sided dolphins using a convolutional neural network and a method to reduce false-positive detections.

The Journal of the Acoustical Society of America
An automatic detector for identifying the clicks and pulsed calls of Pacific white-sided dolphins (Lagenorhynchus obliquidens) was developed using a convolutional neural network architecture for passive acoustic monitoring, particularly in the areas ...

Ensemble learning of deep CNN models and two stage level prediction of Cobb angle on surface topography in adolescents with idiopathic scoliosis.

Medical engineering & physics
This study employs Convolutional Neural Networks (CNNs) as feature extractors with appended regression layers for the non-invasive prediction of Cobb Angle (CA) from Surface Topography (ST) scans in adolescents with Idiopathic Scoliosis (AIS). The ai...

Enhancing wisdom teeth detection in panoramic radiographs using multi-channel convolutional neural network with clinical knowledge.

Computers in biology and medicine
This study presents a novel artificial intelligence approach for detecting wisdom teeth in panoramic radiographs using a multi-channel convolutional neural network (CNN). First, a curated dataset of annotated panoramic dental images was collected, wi...

An explainable adaptive channel weighting-based deep convolutional neural network for classifying renal disorders in computed tomography images.

Computers in biology and medicine
Renal disorders are a significant public health concern and a cause of mortality related to renal failure. Manual diagnosis is subjective, labor-intensive, and depends on the expertise of nephrologists in renal anatomy. To improve workflow efficiency...

Automated annotation of virtual dual stains to generate convolutional neural network for detecting cancer metastases in H&E-stained lymph nodes.

Pathology, research and practice
CONTEXT: Staging cancer patients is crucial and requires analyzing all removed lymph nodes microscopically for metastasis. For this pivotal task, convolutional neural networks (CNN) can reduce workload and improve diagnostic accuracy.