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
stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning
Sign Up to like & getrecommendations! Published in 2025 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2025.1566675
Abstract: The growing availability of spatial transcriptomics data offers key resources for annotating query datasets using reference datasets. However, batch effects, unbalanced reference annotations, and tissue heterogeneity pose significant challenges to alignment analysis. Here, we present… read more here.
Keywords: based supervised; spatial transcriptomics; supervised graph; attention based ... See more keywords