Longitudinal assessment of carotid plaque texture in three-dimensional ultrasound images based on semi-supervised graph-based dimensionality reduction and feature selection
Sign Up to like & getrecommendations! Published in 2020 at "Computers in biology and medicine"
DOI: 10.1016/j.compbiomed.2019.103586
Abstract: With continuous development of therapeutic options for atherosclerosis, image-based biomarkers sensitive to the effect of new interventions are required to be developed for cost-effective clinical evaluation. Although 3D ultrasound measurement of total plaque volume (TPV)… read more here.
Keywords: feature selection; semi supervised; ssgbr; supervised graph ... See more keywords
Self-supervised Graph Representation Learning via Bootstrapping
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2021.03.123
Abstract: Graph neural networks~(GNNs) apply deep learning techniques to graph-structured data and have achieved promising performance in graph representation learning. However, existing GNNs rely heavily on enough labels or well-designed negative samples. To address these issues,… read more here.
Keywords: self supervised; supervised graph; representation learning; graph ... See more keywords
Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2019.2939637
Abstract: Semi-supervised non-negative matrix factorization (NMF) exploits the strengths of NMF in effectively learning local information contained in data and is also able to achieve effective learning when only a small fraction of data is labeled.… read more here.
Keywords: representation; graph regularized; semi supervised; supervised graph ... See more keywords