Výsledky vyhledávání - "Deep-learning image analysis"

EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS

Autoři: S Sujatha Pramod Pandey D Gnana Rajesh

Zdroj: International Journal of Advances in Signal and Image Sciences, Vol 10, Iss 2, Pp 48-57 (2024)

Témata: Computer applications to medicine. Medical informatics, R858-859.7, biomedical imaging, age-related macular degeneration, deep learning, image analysis, macular deterioration, TP248.13-248.65, Biotechnology

Přístupová URL adresa: https://doaj.org/article/91326d11dc4a4b97a5c8e882ef252322

A spheroid whole mount drug testing pipeline with machine-learning based image analysis identifies cell-type specific differences in drug efficacy on a single-cell level

Autoři: Mario Vitacolonna Roman Bruch Richard Schneider a další

Zdroj: BMC Cancer, Vol 24, Iss 1, Pp 1-23 (2024)

Témata: 3D co-culture, 3D drug testing, Deep-learning image analysis, Tumor microenvironment, Single-cell analysis, Drug resistance, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282

Popis souboru: electronic resource

Relation: https://doaj.org/toc/1471-2407

Přístupová URL adresa: https://doaj.org/article/94688b951250495fb6faafe8830e39c4

Deep Learning‐Based Classification of Histone–DNA Interactions Using Drying Droplet Patterns

Autoři: Safoura Vaez Bahar Dadfar Meike Koenig a další

Zdroj: Small Science, Vol 4, Iss 11, Pp n/a-n/a (2024)

Témata: coffee ring, deep learning image analysis, drying droplet, histone–DNA interaction, Materials of engineering and construction. Mechanics of materials, TA401-492

Popis souboru: electronic resource

Relation: https://doaj.org/toc/2688-4046

Přístupová URL adresa: https://doaj.org/article/2b8bc477a1ca4686ad69894c336f2609

Comparison of Deep Transfer Learning Models for the Quantification of Photoelastic Images

Autoři: Seongmin Kim Boo Hyun Nam Young-Hoon Jung

Zdroj: Applied Sciences, Vol 14, Iss 2, p 758 (2024)

Témata: reflection photoelastic method, deep learning image analysis, transfer learning model, prediction performance evaluation, granular materials, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/14/2/758; https://doaj.org/toc/2076-3417; https://doaj.org/article/c31ccff7396446429c98a7e7d3313fb6

Dostupnost: https://doi.org/10.3390/app14020758
https://doaj.org/article/c31ccff7396446429c98a7e7d3313fb6

Deep learning and chemical constraints allow accurate segmentation of µCT data from metamorphic rocks

Autoři: Roberto Emanuele Rizzo Damien Freitas James Gilgannon a další

Témata: Deep Learning, Image Analysis, Synchrotron, Micro-Computed Tomography, Rocks, Faults, Rock Deformation, Rock Mechanics

Popis souboru: application/pdf

Přístupová URL adresa: https://hdl.handle.net/2158/1309606
https://doi.org/10.5194/egusphere-egu23-7120

Segmentation of Petri Plate Images for Automatic Reporting of Urine Culture Tests

Autoři: Bonechi, Simone Bianchini, Monica Mecocci, Alessandro a další

Přispěvatelé: Bonechi, Simone Bianchini, Monica Mecocci, Alessandro a další

Zdroj: Intelligent Systems Reference Library ISBN: 9783030791605

Témata: Automatic urine culture screening, Automatic urine culture screening, Deep learning, Image analysis, Deep learning, 6. Clean water, Image analysis

Popis souboru: application/pdf; STAMPA

Přístupová URL adresa: https://link.springer.com/chapter/10.1007/978-3-030-79161-2_5
https://hdl.handle.net/11365/1156117
https://link.springer.com/chapter/10.1007/978-3-030-79161-2_5
https://doi.org/10.1007/978-3-030-79161-2_5

A Study of The Deep Learning-based Monitoring and Efficient Numerical Modeling Methodologies for Crystallization Processes

Autoři: Wu, Yuanyi

Zdroj: Electronic Thesis and Dissertation Repository

Témata: Shape and size characterization, Process Control and Systems, Deep learning Image analysis, Solution of population balance equation, Crystallization process, Process analytical technology, Automated experiments

Popis souboru: application/pdf

Přístupová URL adresa: https://ir.lib.uwo.ca/etd/7813

Finding and following: a deep learning-based pipeline for tracking platelets during thrombus formation in vivo and ex vivo

Autoři: McGovern, Abigail S. Larsson, Pia Tarlac, Volga a další

Témata: Deep learning; image analysis; platelets; segmentation; thrombosis; tracking, Biomedical Laboratory Science/Technology, Biomedicinsk laboratorievetenskap/teknologi, Article in journal, info:eu-repo/semantics/article, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-203555
Platelets, 0953-7104, 2024, 35:1

Automated estimation of offshore polymetallic nodule abundance based on seafloor imagery using deep learning.

Autoři: Tomczak A Kogut T Kabała K a další

Zdroj: The Science of the total environment [Sci Total Environ] 2024 Dec 15; Vol. 956, pp. 177225. Date of Electronic Publication: 2024 Nov 05.

Způsob vydávání: Journal Article

Informace o časopise: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0330500 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1026 (Electronic) Linking ISSN: 00489697 NLM ISO Abbreviation: Sci Total Environ Subsets: PubMed not MEDLINE; MEDLINE

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

Autoři: Abels, Esther Pantanowitz, Liron Aeffner, Famke a další

Témata: artificial intelligence; computational pathology; convolutional neural networks; digital pathology; deep learning; image analysis; machine learning, Computer Vision and Robotics (Autonomous Systems), Datorseende och robotik (autonoma system), Article, review/survey, info:eu-repo/semantics/article, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160596
Journal of Pathology, 0022-3417, 2019, 249:3, s. 286-294

Convolutional neural network on three orthogonal planes for dynamic texture classification

Autoři: Andrearczyk, Vincent Whelan, Paul F.

Zdroj: Andrearczyk, Vincent and Whelan, Paul F. ORCID: 0000-0001-9230-7656 <

Témata: Image processing, computer vision; deep learning; image analysis; Convolutional Neural Networks; Texture Classification; Dynamic Texture, Article, PeerReviewed

URL: http://doras.dcu.ie/22093/
http://doras.dcu.ie/22093/1/1703.05530.pdf
https://doi.org/10.1016/j.patcog.2017.10.030
http://doras.dcu.ie/22093/1/1703.05530.pdf
https://doi.org/10.1016/j.patcog.2017.10.030