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Face detection based on a human attention guided multi-scale model
Multiscale models are among the cutting-edge technologies used for face detection and recognition. An example is Deformable part-based models (DPMs),...
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MsRAN: a multi-scale residual attention network for multi-model image fusion
Fusion is a critical step in image processing tasks. Recently, deep learning networks have been considerably applied in information fusion. But the...
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An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction
This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The...
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A temporal multi-scale hybrid attention network for sleep stage classification
Sleep is crucial for human health. Automatic sleep stage classification based on polysomnogram (PSG) is meaningful for the diagnosis of sleep...
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mResU-Net: multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI
Brain tumor segmentation is an important direction in medical image processing, and its main goal is to accurately mark the tumor part in brain MRI....
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MSHANet: a multi-scale residual network with hybrid attention for motor imagery EEG decoding
EEG decoding plays a crucial role in the development of motor imagery brain-computer interface. Deep learning has great potential to automatically...
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Multi-scale modeling to investigate the effects of transcranial magnetic stimulation on morphologically-realistic neuron with depression
Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique to activate or inhibit the activity of neurons, and thereby...
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Diagnosis of pulmonary tuberculosis with 3D neural network based on multi-scale attention mechanism
This paper presents a novel multi-scale attention residual network (MAResNet) for diagnosing patients with pulmonary tuberculosis (PTB) by computed...
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Re-UNet: a novel multi-scale reverse U-shape network architecture for low-dose CT image reconstruction
AbstractIn recent years, the growing awareness of public health has brought attention to low-dose computed tomography (LDCT) scans. However, the CT...
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Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study
BackgroundTumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD)....
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Automatic vertebral fracture and three-column injury diagnosis with fracture visualization by a multi-scale attention-guided network
Deep learning methods have the potential to improve the efficiency of diagnosis for vertebral fractures with computed tomography (CT) images. Most...
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Multi-scale characterisation of homologous recombination deficiency in breast cancer
BackgroundHomologous recombination is a robust, broadly error-free mechanism of double-strand break repair, and deficiencies lead to PARP inhibitor...
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Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition
Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy,...
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Using multi-scale genomics to associate poorly annotated genes with rare diseases
BackgroundNext-generation sequencing (NGS) has significantly transformed the landscape of identifying disease-causing genes associated with genetic...
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Context fusion network with multi-scale-aware skip connection and twin-split attention for liver tumor segmentation
AbstractManually annotating liver tumor contours is a time-consuming and labor-intensive task for clinicians. Therefore, automated segmentation is...
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Super-Resolution Reconstruction of CT Images Based on Multi-scale Information Fused Generative Adversarial Networks
The popularization and widespread use of computed tomography (CT) in the field of medicine evocated public attention to the potential radiation...
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Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains
Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine...
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MSFF-Net: Multi-Scale Feature Fusion Network for Gastrointestinal Vessel Segmentation
PurposeThe accurate and automatic segmentation of gastrointestinal wall vessels can help to prevent endoscope tip related perforation. Methods based...
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Integrated multi-omics for rapid rare disease diagnosis on a national scale
Critically ill infants and children with rare diseases need equitable access to rapid and accurate diagnosis to direct clinical management. Over 2...
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Light-Weight Localization and Scale-Independent Multi-gate UNET Segmentation of Left and Right Ventricles in MRI Images
PurposeHeart segmentation in cardiac magnetic resonance images is heavily used during the assessment of left ventricle global function. Automation of...
