Results 11 to 20 of about 187 (133)

Integration of Raman Spectroscopy and Metabolomics for Early Breast Cancer Detection and Classification. [PDF]

Cancer Med
ABSTRACT Breast cancer, now the fourth leading cause of cancer‐related mortality worldwide, necessitates early detection for improved clinical outcomes. Conventional histopathology, though widely used, is invasive and subjective, limiting its utility in early‐stage diagnosis.
Li X, Ren H, Deng Y, Li Y, Hu F.
europepmc   +2 more sources

Satellite Remote Sensing of Alpine Vegetation Dynamics: Challenges and Perspectives. [PDF]

Glob Chang Biol
Satellite greening has become a key tool for monitoring alpine vegetation change, but a positive vegetation‐index trend is not an ecological observation in itself. This perspective shows that interpreting alpine greening requires addressing two sequential challenges: methodological complexity, which can bias trends during image processing, and ...
Bayle A.
europepmc   +2 more sources

Deep Learning Integration in Optical Microscopy: Advancements and Applications. [PDF]

Microsc Res Tech
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Lahari PV, Dutta S, Deeksha H, Patel SA, Dehury B, Mazumder N.   +5 more
europepmc   +2 more sources

Spatial Immunometabolism: Integrating Technologies to Decode Cellular Metabolism in Tissues. [PDF]

Eur J Immunol
This review highlights recent advances that enable spatially resolved analysis of immunometabolism within tissue microenvironments. Integrating mass spectrometry imaging, vibrational microscopy, and spatial omics reveals how metabolic organization shapes immune function in cancer and other pathologies.
Hartmann FJ.
europepmc   +2 more sources

Spectrometer-Less Remote Sensing Image Classification Based on Gate-Tunable van der Waals Heterostructures. [PDF]

Adv Sci (Weinh)
Artificial designed gate‐tunable wide‐spectral 2D‐vdWH GaTe0.5Se0.5/WSe2‐based photodetector, requiring no additional auxiliary components, can achieve an average UV‐Vis‐NIR remote sensing image classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250–1000 nm hyperspectral data (88.72%).
Yu Y, Zhong M, Xiong T, Yang J, Hu P, Long H, Zhou Z, Xin K, Liu YY, Yang J, Qiao J, Liu D, Wei Z.   +12 more
europepmc   +2 more sources

Hyperspectral image non-linear unmixing using joint extrinsic and intrinsic priors with L1/2-norms to non-negative matrix factorisation

Journal of Spectral Imaging, 2022
Hyperspectral unmixing (HU) is one of the most active emerging areas in image processing that estimates the hyperspectral image’s endmember and abundance.
K. Priya, K. K. Rajkumar
doaj   +1 more source

An Efficient Attention-Based Convolutional Neural Network That Reduces the Effects of Spectral Variability for Hyperspectral Unmixing

Applied Sciences, 2022
The purpose of hyperspectral unmixing (HU) is to obtain the spectral features of materials (endmembers) and their proportion (abundance) in a hyperspectral image (HSI).
Baohua Jin, Yunfei Zhu, Wei Huang, Qiqiang Chen, Sijia Li   +4 more
doaj   +1 more source

A Modified Huber Nonnegative Matrix Factorization Algorithm for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021
Hypersepctral unmixing (HU) has been one of the most challenging tasks in hyperspectral image research. Recently, nonnegative matrix factorization (NMF) has shown its superiority in hyperspectral unmixing due to its flexible modeling and little prior ...
Ziyang Guo, Anyou Min, Bing Yang, Junhong Chen, Hong Li   +4 more
doaj   +1 more source

Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing

European Journal of Remote Sensing, 2022
Superpixel-based hyperspectral unmixing (HU) can effectively reduce spectral variability’s influence on unmixing performance. In the superpixel-based HU method, this study proposes a segmentation scale determination method to improve the accuracy of ...
Qiang Guan, Tongyu Xu, Shuai Feng, Fenghua Yu, Kai Song   +4 more
doaj   +1 more source

Curvelet Transform Domain-Based Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Hyperspectral unmixing (HU) is an efficient way to extract component information from mixed pixels in remotely sensed imagery. Nonnegative matrix factorization (NMF) based unmixing methods have been widely used due to their ability to extract endmembers (
Xiang Xu, Jun Li, Shutao Li, Antonio Plaza   +3 more
doaj   +1 more source