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
From Nuclear Receptors to GPCRs: a Deep Transfer Learning Approach for Enhanced Environmental Estrogen Recognition.
Sign Up to like & getrecommendations! Published in 2025 at "Environmental science & technology"
DOI: 10.1021/acs.est.5c02014
Abstract: Environmental estrogens (EEs), as typical endocrine-disrupting chemicals (EDCs), can bind to classic estrogen receptors (ERs) to induce genomic effects, as well as to G protein-coupled estrogen receptor (GPER) located on the membrane, thereby inducing downstream… read more here.
Keywords: transfer learning; deep transfer; nuclear receptors; model ... 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
A novel interpretable deep transfer learning combining diverse learnable parameters for improved T2D prediction based on single-cell gene regulatory networks
Sign Up to like & getrecommendations! Published in 2024 at "Scientific Reports"
DOI: 10.1038/s41598-024-54923-y
Abstract: Accurate deep learning (DL) models to predict type 2 diabetes (T2D) are concerned not only with targeting the discrimination task but also with learning useful feature representation. However, existing DL tools are far from perfect… read more here.
Keywords: transfer learning; deep transfer; single cell; based single ... See more keywords
An MRI-based deep transfer learning radiomics nomogram for predicting meningioma grade
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-01665-0
Abstract: The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set,… read more here.
Keywords: dtl features; transfer learning; deep transfer; dtlr nomogram ... See more keywords