Results 31 to 40 of about 8,230,666 (286)
Contextual Attention Network: Transformer Meets U-Net
, 2022 Currently, convolutional neural networks (CNN) (e.g., U-Net) have become the de facto standard and attained immense success in medical image segmentation. However, as a downside, CNN based methods are a double-edged sword as they fail to build long-range dependencies and global context connections due to the limited receptive field that stems from the ...
Reza Azad +3 more
openaire +2 more sources
EAAU-Net: Enhanced Asymmetric Attention U-Net for Infrared Small Target Detection
Remote Sensing, 2021 Detecting infrared small targets lacking texture and shape information in cluttered environments is extremely challenging. With the development of deep learning, convolutional neural network (CNN)-based methods have achieved promising results in generic ...
Xiaozhong Tong +4 more
doaj +1 more source
Attention Gate ResU-Net for Automatic MRI Brain Tumor Segmentation
IEEE Access, 2020 Brain tumor segmentation technology plays a pivotal role in the process of diagnosis and treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as develop treatment and rehabilitation strategies.
Jianxin Zhang +4 more
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Landmark Regression with Attention U-Net for Assessing Breast Positioning Quality in MLO Mammography View. [PDF]
Diagnostics (Basel)Background/Objectives: This study developed and evaluated a novel Attention U-Net regression model to assess MLO mammography positioning quality, comparing it against a plain U-Net regression baseline (architectural ablation) and a ResNeXt50 ...
Denizoglu N +9 more
europepmc +2 more sources
AResU-Net: Attention Residual U-Net for Brain Tumor Segmentation
, 2020 Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors.
Hengbo Zhang +3 more
core +1 more source
International Journal of Digital Earth, 2023 Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing ...
Hesheng Chen +7 more
doaj +1 more source
Rapid Mapping of Landslides on SAR Data by Attention U-Net
Remote Sensing, 2022 Multiple landslide events are common around the globe. They can cause severe damage to both human lives and infrastructures. Although a huge quantity of research has been shaped to address rapid mapping of landslides by optical Earth Observation (EO) data, various gaps and uncertainties are still present when dealing with cloud obscuration and 24/7 ...
Lorenzo Nava +4 more
openaire +6 more sources
Multi-scale Attention U-Net (MsAUNet): A Modified U-Net Architecture for Scene Segmentation
CoRR, 2020 Despite the growing success of Convolution neural networks (CNN) in the recent past in the task of scene segmentation, the standard models lack some of the important features that might result in sub-optimal segmentation outputs. The widely used encoder-decoder architecture extracts and uses several redundant and low-level features at different steps ...
Soham Chattopadhyay, Hritam Basak
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U-Net_dc: a novel U-Net-based model for endometrial cancer cell image segmentation
, 2023 Mutated cells may constitute a source of cancer. As an effective approach to quantifying the extent of cancer, cell image segmentation is of particular importance for understanding the mechanism of the disease, observing the degree of cancer cell lesions,
Zhanlin Ji (14016624) +6 more
core +1 more source
Fault detection and computation of power in PV cells under faulty conditions using deep-learning
Energy Reports, 2023 Renewable energy is considered to be an alternate option for limiting the consumption of fossil fuel along with reducing environmental pollution. Among the possible renewable energy resources, solar energy is considered to be the key candidate as it is ...
Amir Sohail +5 more
doaj +1 more source