Results 131 to 140 of about 2,218,762 (336)

The Role of Machine Learning to Detect Occult Neck Lymph Node Metastases in Early‐Stage (T1‐T2/N0) Oral Cavity Carcinomas

Head &Neck, EarlyView.
ABSTRACT Objective Oral cavity carcinomas (OCCs) represent roughly 50% of all head and neck cancers. The risk of occult neck metastases for early‐stage OCCs ranges from 15% to 35%, hence the need to develop tools that can support the diagnosis of detecting these neck metastases.
Stefania Troise, Lorenzo Ugga, Maria Esposito, Maria Positano, Andrea Elefante, Serena Capasso, Renato Cuocolo, Raffaele Merola, Umberto Committeri, Vincenzo Abbate, Paola Bonavolontà, Riccardo Nocini, Giovanni Dell’Aversana Orabona   +12 more
wiley   +1 more source

A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy

Head &Neck, EarlyView.
ABSTRACT Background Endoscopy is essential in upper aerodigestive tract (UADT) examination, particularly in the early detection of laryngopharyngeal lesions. However, UADT endoscopy remains operator‐dependent and lacks standardized quality metrics. Recent advancements in artificial intelligence (AI) have generated interest in applications within UADT ...
Celine M. L. H. Wilmes, Arsen Goril BSc, Henri A. M. Marres, David J. Wellenstein, Guido B. van den Broek   +4 more
wiley   +1 more source

A Nordhaus–Gaddum Problem for the Spectral Gap of a Graph

Journal of Graph Theory, EarlyView.
ABSTRACT Let G $G$ be a graph on n $n$ vertices, with complement G¯ $\bar{G}$. The spectral gap of the transition probability matrix of a random walk on G $G$ is used to estimate how fast the random walk becomes stationary. We prove that the larger spectral gap of G $G$ and G¯ $\bar{G}$ is Ω(1∕n) ${\rm{\Omega }}(1\unicode{x02215}n)$.
Sooyeong Kim, Neal Madras
wiley   +1 more source

On distance signless Laplacian spectrum and energy of graphs

Electronic Journal of Graph Theory and Applications, 2018
The distance signless Laplacian spectral radius of a connected graph G is the largest eigenvalue of the distance signless Laplacian matrix of G‎, ‎defined as ‎D‎Q(G) = Tr(G) + D(G)‎, ‎where D(G) is the distance matrix of G and Tr(G) is the diagonal ...
Abdollah Alhevaz, Maryam Baghipur, Ebrahim Hashemi   +2 more
doaj   +1 more source

Laplacian matrix and distance in trees

Kragujevac journal of mathematics, 2004
Laplacian matrix and distance in trees.
Vukičević, Damir, Gutman, Ivan
openaire   +1 more source

A two‐stage model for precise identification and Gleason grading of clinically significant prostate cancer: a hybrid approach

Journal of Medical Radiation Sciences, Volume 72, Issue 1, Page 93-105, March 2025.
This study developed a two‐stage model using radiomics‐based multiparametric MRI and clinical indicators to help identify and grade clinically significant prostate cancer. The model showed promising levels of diagnostic accuracy and predictive performance.
Yuyan Zou, Xuechun Wang, Fen Ma, Xulun Liu, Chunyue Jiao, Zhen Kang, Jingjing Cui, Yang Zhang, Yan Xie, Lei Chen, Ronghua Tian   +10 more
wiley   +1 more source

Robust joint clustering of multi-omics single-cell data via multi-modal high-order neighborhood Laplacian matrix optimization. [PDF]

Bioinformatics, 2023
Jiang H, Zhan S, Ching WK, Chen L.
europepmc   +1 more source

Discrete Chiral Ballistic Polariton Laser

Laser &Photonics Reviews, EarlyView.
Planar microcavities in the strong light–matter coupling regime exhibit exciton‐polariton modes with a strong chiral response toward circularly polarized light. This feature is exploited alongside inherent spin‐orbit coupling of cavity light to generate giant discrete optical vortices in polygonal arrays of exciton‐polariton condensates.
Zuzanna Werner, Andrzej Frączak, Valtýr Kári Daníelsson, Jacek Szczytko, Barbara Piętka, Helgi Sigurðsson   +5 more
wiley   +1 more source

Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification. [PDF]

Sensors (Basel), 2023
Orozco J, Manian V, Alfaro E, Walia H, Dhatt BK.   +4 more
europepmc   +1 more source

Sparse random graphs: regularization and concentration of the Laplacian [PDF]

, 2015
We study random graphs with possibly different edge probabilities in the challenging sparse regime of bounded expected degrees. Unlike in the dense case, neither the graph adjacency matrix nor its Laplacian concentrate around their expectations due to ...
Le, Can M., Levina, Elizaveta, Vershynin, Roman   +2 more
core   +1 more source