Results 91 to 100 of about 6,103 (201)

AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging. [PDF]

Biomed Opt Express, 2023
Jiang Y, Sha H, Liu S, Qin P, Zhang Y.
europepmc   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

Advanced Intelligent Discovery, Volume 2, Issue 1, February 2026.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

A Streamlined Protocol for Developing a Clinicopathological Prediction Model for Patient Survival of Post‐Resection of Pancreatic Cancer

Cancer Medicine, Volume 15, Issue 2, February 2026.
We have identified the spatial distribution of MHC‐I, CD4, CD8, CD19, PAK4, and LC3B using multiplex immunostaining. We then combined the data of multiplex immunostaining with clinical pathological data for the software analysis to create a model that can be used to predict the post‐resection survival of pancreatic cancer patients.
Yi Ma, Eunice Lee, Khashayar Asadi, Mehrdad Nikfarjam, Hong He   +4 more
wiley   +1 more source

Probabilistic Mixture Model-Based Spectral Unmixing

Applied Sciences
Spectral unmixing attempts to decompose a spectral ensemble into the constituent pure spectral signatures (called endmembers) along with the proportion of each endmember.
Oliver Hoidn, Aashwin Ananda Mishra, Apurva Mehta   +2 more
doaj   +1 more source

Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging. [PDF]

Sci Rep, 2023
Cano C, Matos C, Gholampour A, van Sambeek M, Lopata R, Wu M.   +5 more
europepmc   +1 more source

Shape Matters: Unlocking Transport Histories in Fine‐Grained Aeolian Sediments With Endmember Modeling of Size–Shape Distributions

Earth and Space Science, Volume 13, Issue 2, February 2026.
Abstract The transport medium, mode, energy, and distance are recorded in the grain‐size and grain‐shape distributions in a sedimentary deposit. While grain‐size analysis has long been used in sedimentology, grain‐shape analysis is increasingly recognized as a valuable tool for reconstructing sedimentary processes and palaeoenvironments.
P. P. Stark, M. A. Prins, C. J. Beets, H. Tang, R. T. van Balen, A. P. Kaakinen   +5 more
wiley   +1 more source

Robust Multiscale Spectral–Spatial Regularized Sparse Unmixing for Hyperspectral Imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
With the aid of endmember spectral libraries, sparse unmixing plays a critical role in interpreting hyperspectral remote sensing data. Integrating spatial clues from hyperspectral data into sparse unmixing frameworks is pivotal for enhancing unmixing ...
Ke Wang, Lei Zhong, Jiajun Zheng, Shaoquan Zhang, Fan Li, Chengzhi Deng, Jingjing Cao, Dingli Su   +7 more
doaj   +1 more source

Multi-resolution terrestrial hyperspectral dataset for spectral unmixing problems. [PDF]

Data Brief, 2022
Manohar Kumar CVSS, Jha SS, Nidamanuri RR, Dadhwal VK.   +3 more
europepmc   +1 more source

Darkened Snow Triggers Different Snowmelt Responses Over Contrasting Water Years in Great Salt Lake Headwater Basins

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Snow in the Great Salt Lake Basin is a vital resource for regional agriculture, municipal water use, and the Great Salt Lake. Accumulation of light absorbing particles (LAPs) on mountain snowpacks results in lower albedos and earlier melt compared to clean snow.
Otto I. Lang, Joachim Meyer, J. Michelle Hu, S. McKenzie Skiles   +3 more
wiley   +1 more source

Linear Spatial Misregistration Detection and Correction Based on Spectral Unmixing for FAHI Hyperspectral Imagery. [PDF]

Sensors (Basel), 2022
Zhang X, Cheng X, Xue T, Wang Y.
europepmc   +1 more source