Articles with "convolutional neural" as a keyword



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Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine.

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Published in 2023 at "JAMA dermatology"

DOI: 10.1001/jamadermatol.2023.0905

Abstract: Importance Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of the first neural networks for clinical use, prospective studies demonstrating benefits of human… read more here.

Keywords: human machine; melanocytic lesions; machine; convolutional neural ... See more keywords
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Deep learning‐based convolutional neural network for intramodality brain MRI synthesis

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Published in 2022 at "Journal of Applied Clinical Medical Physics"

DOI: 10.1002/acm2.13530

Abstract: Abstract Purpose The existence of multicontrast magnetic resonance (MR) images increases the level of clinical information available for the diagnosis and treatment of brain cancer patients. However, acquiring the complete set of multicontrast MR images… read more here.

Keywords: neural network; brain mri; brain; convolutional neural ... See more keywords
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Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat?

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Published in 2022 at "Advanced Science"

DOI: 10.1002/advs.202204723

Abstract: The interpretability of deep neural networks has attracted increasing attention in recent years, and several methods have been created to interpret the “black box” model. Fundamental limitations remain, however, that impede the pace of understanding… read more here.

Keywords: neural networks; networks makes; convolutional neural; interpretation ... See more keywords
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A medium‐weight deep convolutional neural network‐based approach for onset epileptic seizures classification in EEG signals

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Published in 2022 at "Brain and Behavior"

DOI: 10.1002/brb3.2763

Abstract: Epileptic condition can be detected in EEG data seconds before it occurs, according to evidence. To overcome the related long‐term mortality and morbidity from epileptic seizures, it is critical to make an initial diagnosis, uncover… read more here.

Keywords: onset epileptic; deep convolutional; epileptic seizures; convolutional neural ... See more keywords
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EEG‐based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features

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Published in 2019 at "Human Brain Mapping"

DOI: 10.1002/hbm.24724

Abstract: Prognostication for comatose patients after cardiac arrest is a difficult but essential task. Currently, visual interpretation of electroencephalogram (EEG) is one of the main modality used in outcome prediction. There is a growing interest in… read more here.

Keywords: outcome; outcome prediction; eeg based; convolutional neural ... See more keywords
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Testing a convolutional neural network-based hippocampal segmentation method in a stroke population.

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Published in 2020 at "Human brain mapping"

DOI: 10.1002/hbm.25210

Abstract: As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in… read more here.

Keywords: hippocampal segmentation; segmentation; method; convolutional neural ... See more keywords
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Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network

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Published in 2022 at "International Journal of Imaging Systems and Technology"

DOI: 10.1002/ima.22699

Abstract: Automatic lesion segmentation is a key phase of skin lesion analysis that significantly increases the performance of subsequent classification steps. Segmentation is a highly complex task due to the varying nature of lesions, such as… read more here.

Keywords: network; skin lesion; convolutional neural; segmentation ... See more keywords
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A hybrid convolutional neural network model to detect COVID‐19 and pneumonia using chest X‐ray images

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Published in 2022 at "International Journal of Imaging Systems and Technology"

DOI: 10.1002/ima.22829

Abstract: A hybrid convolutional neural network (CNN)‐based model is proposed in the article for accurate detection of COVID‐19, pneumonia, and normal patients using chest X‐ray images. The input images are first pre‐processed to tackle problems associated… read more here.

Keywords: neural network; covid pneumonia; hybrid convolutional; convolutional neural ... See more keywords
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Convolutional neural network-based malaria diagnosis from focus stack of blood smear images acquired using custom-built slide scanner.

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Published in 2018 at "Journal of biophotonics"

DOI: 10.1002/jbio.201700003

Abstract: The present paper introduces a focus stacking-based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images… read more here.

Keywords: focus stack; malaria; blood smear; convolutional neural ... See more keywords
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Brain tumor segmentation in multi‐spectral MRI using convolutional neural networks (CNN)

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Published in 2018 at "Microscopy Research and Technique"

DOI: 10.1002/jemt.22994

Abstract: A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same… read more here.

Keywords: network; segmentation; convolutional neural; brain tumor ... See more keywords
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Editorial for “Convolutional neural network for accelerating the computation of the extended Tofts model in dynamic contrast‐enhanced magnetic resonance imaging”

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Published in 2021 at "Journal of Magnetic Resonance Imaging"

DOI: 10.1002/jmri.27545

Abstract: Editorial for “Convolutional neural network for accelerating the computation of the extended Tofts model in dynamic contrastenhanced magnetic resonance imaging” Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) can be a valuable tool for quantifying blood–brain… read more here.

Keywords: network; computation; magnetic resonance; time ... See more keywords