Total Variation Constrained Graph-Regularized Convex Non-Negative Matrix Factorization for Data Representation
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2020.3047576
Abstract: We propose a novel NMF algorithm, named Total Variation constrained Graph-regularized Convex Non-negative Matrix Factorization (TV-GCNMF), to incorporate total variation and graph Laplacian with convex NMF. In this model, the feature details of the data… read more here.
Keywords: graph regularized; variation constrained; total variation; constrained graph ... See more keywords
Diagnosis of Glioblastoma Multiforme Progression via Interpretable Structure-Constrained Graph Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Medical Imaging"
DOI: 10.1109/tmi.2022.3202037
Abstract: Glioblastoma multiforme (GBM) is the most common type of brain tumors with high recurrence and mortality rates. After chemotherapy treatment, GBM patients still show a high rate of differentiating pseudoprogression (PsP), which is often confused… read more here.
Keywords: interpretable structure; diagnosis; progression; constrained graph ... See more keywords