Deep learning: an update for radiologists
Deep learning is a class of machine learning methods that has been successful in computer
vision. Unlike traditional machine learning methods that require hand-engineered feature …
vision. Unlike traditional machine learning methods that require hand-engineered feature …
A new end-to-end multi-dimensional CNN framework for land cover/land use change detection in multi-source remote sensing datasets
ST Seydi, M Hasanlou, M Amani - Remote Sensing, 2020 - mdpi.com
The diversity of change detection (CD) methods and the limitations in generalizing these
techniques using different types of remote sensing datasets over various study areas have …
techniques using different types of remote sensing datasets over various study areas have …
Explainable COVID-19 detection on chest X-rays using an end-to-end deep convolutional neural network architecture
The coronavirus pandemic is spreading around the world. Medical imaging modalities such
as radiography play an important role in the fight against COVID-19. Deep learning (DL) …
as radiography play an important role in the fight against COVID-19. Deep learning (DL) …
SOME/IP intrusion detection using deep learning-based sequential models in automotive ethernet networks
Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols
vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning …
vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning …
The structural similarity index for IMRT quality assurance: radiomics‐based error classification
C Ma, R Wang, S Zhou, M Wang, H Yue… - Medical …, 2021 - Wiley Online Library
Purpose The implementation of radiomics and machine learning (ML) techniques on
analyzing two‐dimensional gamma maps has been demonstrated superior to the …
analyzing two‐dimensional gamma maps has been demonstrated superior to the …
Elevating the park experience: Exploring asymmetric relationships in visitor satisfaction at Canadian national parks
Visitor satisfaction in national parks results from service and experience quality attributes
and influences visitor retention. Therefore, satisfaction and its determinants are essential for …
and influences visitor retention. Therefore, satisfaction and its determinants are essential for …
Toward patient-specific prediction of ablation strategies for atrial fibrillation using deep learning
Atrial fibrillation (AF) is a common cardiac arrhythmia that affects 1% of the population
worldwide and is associated with high levels of morbidity and mortality. Catheter ablation …
worldwide and is associated with high levels of morbidity and mortality. Catheter ablation …
Parametric and non-parametric analyses for pedestrian crash severity prediction in Great Britain
The study aims to investigate the factors that are associated with fatal and severe vehicle–
pedestrian crashes in Great Britain by develo** four parametric models and five non …
pedestrian crashes in Great Britain by develo** four parametric models and five non …
Multi-expert deep networks for multi-disease detection in retinal fundus images
Automatic diagnosis of eye diseases from retinal fundus images is quite challenging.
Common public datasets include images of subjects with multiple diseases with uneven …
Common public datasets include images of subjects with multiple diseases with uneven …
AWSMOTE: An SVM‐Based Adaptive Weighted SMOTE for Class‐Imbalance Learning
JB Wang, CA Zou, GH Fu - Scientific Programming, 2021 - Wiley Online Library
In class‐imbalance learning, Synthetic Minority Oversampling Technique (SMOTE) is a
widely used technique to tackle class‐imbalance problems from the data level, whereas …
widely used technique to tackle class‐imbalance problems from the data level, whereas …