Prognostic aspects of lymphovascular invasion in localized gastric cancer: new insights into the radiomics and deep transfer learning from contrast-enhanced CT imaging
Sign Up to like & getrecommendations! Published in 2021 at "Abdominal Radiology"
DOI: 10.1007/s00261-021-03309-z
Abstract: Lymphovascular invasion (LVI) is a factor significantly impacting treatment and outcome of patients with gastric cancer (GC). We aimed to investigate prognostic aspects of a preoperative LVI prediction in GC using radiomics and deep transfer… read more here.
Keywords: gastric cancer; deep transfer; radiomics deep; transfer learning ... See more keywords
Deep transfer learning in human–robot interaction for cognitive and physical rehabilitation purposes
Sign Up to like & getrecommendations! Published in 2021 at "Pattern Analysis and Applications"
DOI: 10.1007/s10044-021-00988-8
Abstract: This paper presents the extraction of the emotional signals from traumatic brain-injured (TBI) patients through the analysis of facial features and implementation of the effective emotion-recognition model through the Pepper robot to assist in the… read more here.
Keywords: transfer learning; deep transfer; robot; rehabilitation ... See more keywords
Recognizing COVID-19 from chest X-ray images for people in rural and remote areas based on deep transfer learning model
Sign Up to like & getrecommendations! Published in 2022 at "Multimedia Tools and Applications"
DOI: 10.1007/s11042-022-12030-y
Abstract: In this article, we propose Deep Transfer Learning (DTL) Model for recognizing covid-19 from chest x-ray images. The latter is less expensive, easily accessible to populations in rural and remote areas. In addition, the device… read more here.
Keywords: class; deep transfer; ray images; covid chest ... See more keywords
User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation
Sign Up to like & getrecommendations! Published in 2019 at "User Modeling and User-Adapted Interaction"
DOI: 10.1007/s11257-019-09248-1
Abstract: Building predictive models for human-interactive systems is a challenging task. Every individual has unique characteristics and behaviors. A generic human–machine system will not perform equally well for each user given the between-user differences. Alternatively, a… read more here.
Keywords: adaptive models; using deep; training data; deep transfer ... See more keywords
A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset
Sign Up to like & getrecommendations! Published in 2021 at "Cognitive Computation"
DOI: 10.1007/s12559-020-09802-9
Abstract: Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With… read more here.
Keywords: learning models; deep transfer; study; neutrosophic set ... See more keywords
A systematic review of deep transfer learning for machinery fault diagnosis
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.04.045
Abstract: Abstract With the popularization of the intelligent manufacturing, much attention has been paid in such intelligent computing methods as deep learning ones for machinery fault diagnosis. Thanks to the development of deep learning models, the… read more here.
Keywords: transfer learning; machinery fault; deep transfer; fault diagnosis ... See more keywords
A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Systems Architecture"
DOI: 10.1016/j.sysarc.2020.101830
Abstract: Abstract Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The spreading and infection factors of this disease are very high. A huge number of people from most of the countries are infected… read more here.
Keywords: transfer learning; deep transfer; edge computing; edge ... See more keywords
Improving Compound Activity Classification via Deep Transfer and Representation Learning
Sign Up to like & getrecommendations! Published in 2022 at "ACS Omega"
DOI: 10.1021/acsomega.1c06805
Abstract: Recent advances in molecular machine learning, especially deep neural networks such as graph neural networks (GNNs), for predicting structure–activity relationships (SAR) have shown tremendous potential in computer-aided drug discovery. However, the applicability of such deep… read more here.
Keywords: deep transfer; performance; classification; transfer ... See more keywords
DeepMiceTL: a deep transfer learning based prediction of mice cardiac conduction diseases using early electrocardiograms.
Sign Up to like & getrecommendations! Published in 2023 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbad109
Abstract: Cardiac conduction disease is a major cause of morbidity and mortality worldwide. There is considerable clinical significance and an emerging need of early detection of these diseases for preventive treatment success before more severe arrhythmias… read more here.
Keywords: conduction; transfer learning; deep transfer; cardiac conduction ... See more keywords
Prediction of melanoma Breslow thickness using Deep Transfer Learning Algorithms.
Sign Up to like & getrecommendations! Published in 2023 at "Clinical and experimental dermatology"
DOI: 10.1093/ced/llad107
Abstract: BACKGROUND The distinction between in situ (MIS) or invasive melanoma is challenging even for expert dermatologists. The use of pretrained convolutional neural networks (CNNs) as ancillary decision systems needs further research. OBJECTIVE To develop, validate… read more here.
Keywords: learning algorithms; melanoma; mis invasive; transfer learning ... See more keywords
A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2018.2890566
Abstract: Digital twin is a significant way to achieve smart manufacturing, and provides a new paradigm for fault diagnosis. Traditional data-based fault diagnosis methods mostly assume that the training data and test data are following the… read more here.
Keywords: diagnosis; digital twin; transfer learning; using deep ... See more keywords