Results 41 to 50 of about 16,808 (228)

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images [PDF]

open access: yes, 2018
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA
Abdel-Rahman, Mohamed H.   +740 more
core   +3 more sources

Micromachined Double‐Membrane Mechanically Tunable Metamaterial for Thermal Infrared Filtering

open access: yesAdvanced Photonics Research, Volume 6, Issue 5, May 2025.
Herein, a mechanically tunable double‐layer plasmonic metamaterial leveraging the extraordinary optical transmission effect observed in subwavelength arrays of openings within thin metal layers is presented. The concept is experimentally validated by integrating the proposed metamaterial structure into an electrostatic parallel‐plate actuator to create
Oleg Bannik   +7 more
wiley   +1 more source

Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images

open access: yesJournal of Pathology Informatics, 2019
Background: Tumor programmed death-ligand 1 (PD-L1) status is useful in determining which patients may benefit from programmed death-1 (PD-1)/PD-L1 inhibitors.
Lingdao Sha   +9 more
doaj   +1 more source

Integrating Spatial Proteogenomics in Cancer Research

open access: yesAdvanced Science, EarlyView.
Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang   +13 more
wiley   +1 more source

A novel deep learning-based algorithm combining histopathological features with tissue areas to predict colorectal cancer survival from whole-slide images

open access: yesJournal of Translational Medicine, 2023
Background Many methodologies for selecting histopathological images, such as sample image patches or segment histology from regions of interest (ROIs) or whole-slide images (WSIs), have been utilized to develop survival models.
Yan-Jun Li   +4 more
doaj   +1 more source

Detecting Rainfall Onset Using Sky Images

open access: yes, 2016
Ground-based sky cameras (popularly known as Whole Sky Imagers) are increasingly used now-a-days for continuous monitoring of the atmosphere. These imagers have higher temporal and spatial resolutions compared to conventional satellite images.
Dev, Soumyabrata   +3 more
core   +1 more source

The selection, appraisal and retention of digital scientific data: dighlights of an ERPANET/CODATA workshop [PDF]

open access: yes, 2004
CODATA and ERPANET collaborated to convene an international archiving workshop on the selection, appraisal, and retention of digital scientific data, which was held on 15-17 December 2003 at the Biblioteca Nacional in Lisbon, Portugal.
Anderson, W.   +3 more
core   +1 more source

Multi‐Scale Mapping of Gene Expression from Whole‐slide Images for Identifying Phenotype‐Associated Subpopulations

open access: yesAdvanced Science, EarlyView.
BiSCALE: A pathology‐driven deep learning framework for multi‐scale gene expression prediction from whole‐slide images. It accurately infers bulk and near‐cellular spot‐level expression, links predictions to clinical phenotypes, identifies disease‐associated niches, and enables applications in risk stratification and cell‐identity annotation, providing
Hailong Zheng   +8 more
wiley   +1 more source

Learning to Segment Breast Biopsy Whole Slide Images

open access: yes, 2017
We trained and applied an encoder-decoder model to semantically segment breast biopsy images into biologically meaningful tissue labels. Since conventional encoder-decoder networks cannot be applied directly on large biopsy images and the different sized
Bartlett, Jamen   +5 more
core   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

open access: yesAdvanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu   +4 more
wiley   +1 more source

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