Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine.
Sign Up to like & getrecommendations! 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
Deep learning‐based convolutional neural network for intramodality brain MRI synthesis
Sign Up to like & getrecommendations! 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
ICU‐EEG Pattern Detection by a Convolutional Neural Network
Sign Up to like & getrecommendations! Published in 2025 at "Annals of Clinical and Translational Neurology"
DOI: 10.1002/acn3.70164
Abstract: Patients in the intensive care unit (ICU) often require continuous EEG (cEEG) monitoring due to the high risk of seizures and rhythmic and periodic patterns (RPPs). However, interpreting cEEG in real time is resource‐intensive and… read more here.
Keywords: eeg pattern; convolutional neural; neural network; icu eeg ... See more keywords
Semantic Interpretation for Convolutional Neural Networks: What Makes a Cat a Cat?
Sign Up to like & getrecommendations! 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
Cell culture product quality attribute prediction using convolutional neural networks and Raman spectroscopy
Sign Up to like & getrecommendations! Published in 2024 at "Biotechnology and Bioengineering"
DOI: 10.1002/bit.28646
Abstract: Advanced process control in the biopharmaceutical industry often lacks real‐time measurements due to resource constraints. Raman spectroscopy and Partial Least Squares (PLS) models are often used to monitor bioprocess cultures in real‐time. In spite of… read more here.
Keywords: quality; spectroscopy; convolutional neural; raman spectroscopy ... See more keywords
A medium‐weight deep convolutional neural network‐based approach for onset epileptic seizures classification in EEG signals
Sign Up to like & getrecommendations! 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
Detection the quality of pumpkin seeds based on terahertz coupled with convolutional neural network
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Chemometrics"
DOI: 10.1002/cem.3547
Abstract: Pumpkin seeds are nutritious and have some medicinal value. However, the mold and sprouting are produced during the storage of pumpkin seeds. Food safety and quality problems may be caused if they are not removed… read more here.
Keywords: detection; convolutional neural; quality pumpkin; spectral data ... See more keywords
EEG‐based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features
Sign Up to like & getrecommendations! 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
Testing a convolutional neural network-based hippocampal segmentation method in a stroke population.
Sign Up to like & getrecommendations! 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
Skin lesion segmentation using an improved framework of encoder‐decoder based convolutional neural network
Sign Up to like & getrecommendations! 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
A hybrid convolutional neural network model to detect COVID‐19 and pneumonia using chest X‐ray images
Sign Up to like & getrecommendations! 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