Results 41 to 50 of about 12,062 (243)
Heterogeneous regularization-based tensor subspace clustering for hyperspectral band selection [PDF]
Band selection (BS) reduces effectively the spectral dimension of a hyperspectral image (HSI) by selecting relatively few representative bands, which allows efficient processing in subsequent tasks.
Zhang, Hongyan +3 more
core +3 more sources
Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection
In this paper, a novel unsupervised band selection (BS) criterion based on maximizing representativeness and minimizing redundancy (MRMR) is proposed for selecting a set of informative bands to represent the whole hyperspectral image cube.
Wenqiang Zhang +2 more
doaj +1 more source
In this explorative study, the abundance of circular RNA molecules in bone marrow stem cells was found to be elevated in patients with high‐risk myelodysplastic neoplasms, and to be associated with an increased risk of progression to acute myeloid leukemia.
Eileen Wedge +17 more
wiley +1 more source
Keratin 19 (KRT19) is overexpressed in high‐grade serous ovarian cancer with high levels of Kallikrein‐related peptidases (KLK) 4–7 and is associated with poor survival. In vivo analyses demonstrate that elevated KRT19 increases peritoneal tumour burden.
Sophia Bielesch +13 more
wiley +1 more source
Compressive sensing (CS) has received considerable interest in hyperspectral sensing. Recent articles have also exploited the benefits of CS in hyperspectral image classification (HSIC) in the compressively sensed band domain (CSBD).
C. J. Della Porta, Chein-I Chang
doaj +1 more source
Multi-user millimeter-wave communication relies on narrow beams and dense cell deployments to ensure reliable connectivity. However, tracking optimal beams for multiple mobile users across multiple base stations (BSs) results in significant signaling overhead.
Kehui Li +5 more
openaire +2 more sources
Spatial residual clustering and entropy based ranking for hyperspectral band selection
Though the Hyper-spectral images (HSI) are associated with rich spectral information for discriminating the class-specific objects, the high dimensional data generates Hughes effect for additional processing. So, during pre-processing, band Selection (BS)
Kishore Raju K. +2 more
doaj +1 more source
Circulating tumor cells (CTCs) and plasma cell‐free DNA (cfDNA) were analyzed to detect ESR1 mutations and methylation in patients with advanced breast cancer. CTC‐derived DNA showed higher sensitivity for mutation detection and revealed complementary genetic and epigenetic alterations, highlighting the added value of CTC analysis for understanding ...
Dimitra Stergiopoulou +12 more
wiley +1 more source
TFC: A Series of Band Selection Methods for Hyperspectral Target Detection
The abundant spectral information provided by hyperspectral images (HSIs) greatly benefits target detection (TD), but it introduces a large amount of data redundancy, which greatly increases the complexity of data processing.
Qianghui Wang +3 more
doaj +1 more source
Liquid biopsy‐based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis
This schematic outlines the workflow from biomarker identification to duplex MethyLight assay validation for epithelial ovarian cancer diagnosis using cfDNA‐based liquid biopsy. Initial screening of hypermethylated CpG candidates (cg02957270, cg10061138 cg00480298, COL2A1) was performed in tissue using ARMS‐PCR, COBRA, qPCR and image analysis. Selected
Deepa Bisht +3 more
wiley +1 more source

