Results 71 to 80 of about 1,407 (163)

Empowering Biomedical Research with Foundation Models in Computational Microscopy: A Systematic Review

Advanced Intelligent Systems, Volume 7, Issue 11, November 2025.
The integration of foundation models into computational microscopy revolutionizes biomedical research by enhancing imaging resolution, accelerating data analysis, and enabling real‐time biological interpretation. This systematic review critically examines recent advancements, highlights translational challenges, and discusses the transformative ...
Di Ding, Tianliang Yao, Genyi Feng, Xueqing Gao, Xusen Sun, Rong Luo   +5 more
wiley   +1 more source

Efficient Progressive Mamba Model for Hyperspectral Sequence Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, deep learning-based hyperspectral unmixing has increasingly incorporated spatial information to improve performance. However, the extent of spatial information introduced involves a complex tradeoff: too little offers limited gains ...
Yang Liu, Shujun Liu, Huajun Wang
doaj   +1 more source

Spatial Immunometabolism: Integrating Technologies to Decode Cellular Metabolism in Tissues

European Journal of Immunology, Volume 55, Issue 11, November 2025.
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.
Felix J. Hartmann
wiley   +1 more source

Remote sensing of soil organic carbon in varied tillage‐crop systems

Journal of Environmental Quality, Volume 54, Issue 6, Page 1535-1547, November/December 2025.
Abstract The use of remote sensing (RS) to estimate soil organic carbon (SOC) in cropland has become increasingly important to producers, researchers, and policy makers to assess soil and plant health across spatially variable landscapes. Yet, RS estimation of cropland SOC is challenging, particularly when mixed crop residues and soils are present. Our
Amy L. Zoller, Girma Birru, Tulsi Kharel, Virginia L. Jin, Marty R. Schmer, Ariel Freidenreich, Brian Wardlow, Tim Kettler, Tekleab Gala   +8 more
wiley   +1 more source

Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Spectral unmixing is a significant challenge in hyperspectral image processing. Existing unmixing methods utilize prior knowledge about the abundance distribution to solve the regularization optimization problem, where the difficulty lies in choosing ...
Li Wang, Xiaohua Zhang, Jinhua Zhang, Hua Dong, Hongyun Meng, Licheng Jiao   +5 more
doaj   +1 more source

CResDAE: A Deep Autoencoder with Attention Mechanism for Hyperspectral Unmixing

Remote Sensing
Hyperspectral unmixing aims to extract pure spectral signatures (endmembers) and estimate their corresponding abundance fractions from mixed pixels, enabling quantitative analysis of surface material composition.
Chong Zhao, Jinlin Wang, Qingqing Qiao, Kefa Zhou, Jiantao Bi, Qing Zhang, Wei Wang, Dong Li, Tao Liao, Chao Li, Heshun Qiu, Guangjun Qu   +11 more
doaj   +1 more source

Hyperspectral Unmixing With Multi-Scale Convolution Attention Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral unmixing is to decompose the mixed pixel into the spectral signatures (endmembers) with their corresponding abundances. However, the ignorance of endmember variability in hyperspectral unmixing results in low performance.
Sheng Hu, Huali Li
doaj   +1 more source

Mapping Peatlands Combing Deep Learning With Sparse Spectral Unmixing Based on Zhuhai-1 Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The mixed pixel problem, arising from the complex vegetation types of peatlands, poses a significant challenge for remote sensing-based peatland mapping.
Yulin Xu, Xiaodong Na
doaj   +1 more source

Tissue Classification of Breast Cancer by Hyperspectral Unmixing. [PDF]

Cancers (Basel), 2023
Jong LS, Post AL, Veluponnar D, Geldof F, Sterenborg HJCM, Ruers TJM, Dashtbozorg B.   +6 more
europepmc   +1 more source

Spectral-Spatial Hyperspectral Unmixing Using Multitask Learning

IEEE Access, 2019
Hyperspectral unmixing is an important and challenging task in the field of remote sensing which arises when the spatial resolution of sensors is insufficient for the separation of spectrally distinct materials.
Burkni Palsson, Johannes R. Sveinsson, Magnus O. Ulfarsson   +2 more
doaj   +1 more source