Results 111 to 120 of about 930,547 (198)

Practical considerations for pathologists when selecting digital pathology systems: an ESDIP guide

The Journal of Pathology, Volume 270, Issue 3, Page 328-341, November 2026.
Abstract The implementation of digital pathology (DP) has been reported across academic, public, and private laboratory settings, providing valuable insights into workflow transformation. However, comparatively little guidance exists on how DP systems should be evaluated prior to acquisition and deployment.
Diana Montezuma, Vincenzo L'Imperio, David Ameisen, Rita Carvalho, Peter Boor, Sara P Oliveira, Nadieh Khalili, Inti Zlobec, Norman Zerbe, Jordi Temprana‐Salvador   +9 more
wiley   +1 more source

S2M-Net: Dynamic Hyperspectral Unmixing Network Integrating Spectral Sequence Mamba and Local Spatial–Spectral Awareness

Remote Sensing
Hyperspectral unmixing aims to extract pure endmembers and their corresponding abundance from mixed pixels. Existing deep learning-based unmixing methods predominantly rely on convolutional neural networks (CNNs) or Transformer architectures.
Yongqing Yang, Mengmeng Xu, Weidong Zhang, Ji Zhang, Yuquan Gan   +4 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

Protein Corona Engineering of Lipid Nanoparticles Enables Efficient Functional Gene Silencing in the Liver

Small, Volume 22, Issue 62, 6 November 2026.
By shaping the protein corona, lipid composition directs LNP tropism and enables the preferential delivery of a TIMP1‐targeting siRNA to liver macrophages. In a murine model of primary biliary cholangitis, this strategy promoted macrophage reprogramming and attenuated liver fibrosis.
Naths G. Sukubo, Serena Renzi, Martina B. Violatto, Leila Altigieri, Anna L. Capriotti, Andrea Cerrato, Aldo Laganà, Luca Digiacomo, Erica Quagliarini, Antonio Minopoli, Massimiliano Papi, Daniela Pozzi, Carlo A. Scirè, Pietro Invernizzi, Paolo Bigini, Giulio Caracciolo   +15 more
wiley   +1 more source

Deep NMF and Autoencoder: A Comparative Analysis for Hyperspectral Unmixing Using Prisma Real Images

In hyperspectral data, mixed pixels are frequent due to the low-medium spatial resolution of the imaging spectrometer, or to intimate mixing effects. Hence the process of blind hyperspectral unmixing, which separates the pixel spectra into a collection ...
Nicoletta Del Buono, Gaetano Settembre, Nicolò Taggio, Flavia Esposito   +3 more
core   +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

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, Volume 28, Issue 20, 21 October 2026.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source

Spectral Unmixing using Machine Learning

Spectral Unmixing is an important field of study nowadays which focuses on gener ating fractional abundance of each pixel into constituent materials .In this thesis we have tried to unmix each pixel into three end members namely glacial lake,debris and ...
Dhar, Debashis
core   +1 more source

Comparison of Imaging Models for Spectral Unmixing in Oil Painting. [PDF]

Sensors (Basel), 2021
Grillini F, Thomas JB, George S.
europepmc   +1 more source

Spectral Cytometry: unmixing autofluorescence

Data prepared for the 2024 Babraham Spectral Symposium.Mouse samples stained with an unoptimized panel intended to create difficulties in spectral unmixing. Acquired on a 5-laser Cytek Aurora.
Burton, O (via Mendeley Data)
core   +1 more source