BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

144 related articles for article (PubMed ID: 34283157)

  • 1. Asbestos Detection with Fluorescence Microscopy Images and Deep Learning.
    Cai C; Nishimura T; Hwang J; Hu XM; Kuroda A
    Sensors (Basel); 2021 Jul; 21(13):. PubMed ID: 34283157
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Differential Counting of Asbestos Using Phase Contrast and Fluorescence Microscopy.
    Nishimura T; Alexandrov M; Ishida T; Hirota R; Ikeda T; Sekiguchi K; Kuroda A
    Ann Occup Hyg; 2016 Nov; 60(9):1104-1115. PubMed ID: 27671738
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Deep learning for asbestos counting.
    Rabiee A; Della Ventura G; Mirzapour F; Malinconico S; Bellagamba S; Lucci F; Paglietti F
    J Hazard Mater; 2023 Aug; 455():131590. PubMed ID: 37178531
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Rapid fiber-detection technique by artificial intelligence in phase-contrast microscope images of simulated atmospheric samples.
    Yamamoto T; Iwasaki K; Iida Y; Yuki KI; Nakaji F; Yamashiro H; Toyoguchi T; Terazono A
    Ann Work Expo Health; 2024 Apr; 68(4):420-426. PubMed ID: 38438299
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Evaluation of sensitivity of fluorescence-based asbestos detection by correlative microscopy.
    Ishida T; Alexandrov M; Nishimura T; Minakawa K; Hirota R; Sekiguchi K; Kohyama N; Kuroda A
    J Fluoresc; 2012 Jan; 22(1):357-63. PubMed ID: 21932006
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models.
    Abdurahman F; Fante KA; Aliy M
    BMC Bioinformatics; 2021 Mar; 22(1):112. PubMed ID: 33685401
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Tile-based microscopic image processing for malaria screening using a deep learning approach.
    Shewajo FA; Fante KA
    BMC Med Imaging; 2023 Mar; 23(1):39. PubMed ID: 36949382
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Evaluation of the dark-medium objective lens in counting asbestos fibers by phase-contrast microscopy.
    Lee EG; Nelson JH; Kashon ML; Harper M
    Ann Occup Hyg; 2015 Jun; 59(5):616-28. PubMed ID: 25737333
    [TBL] [Abstract][Full Text] [Related]  

  • 9. An inter-laboratory study to determine the effectiveness of procedures for discriminating amphibole asbestos fibers from amphibole cleavage fragments in fiber counting by phase-contrast microscopy.
    Harper M; Lee EG; Slaven JE; Bartley DL
    Ann Occup Hyg; 2012 Jul; 56(6):645-59. PubMed ID: 22456032
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Precision and accuracy of asbestos fiber counting by phase contrast microscopy.
    Pang TW
    AIHAJ; 2000; 61(4):529-38. PubMed ID: 10976683
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Segmentation of trabecular bone microdamage in Xray microCT images using a two-step deep learning method.
    Caron R; Londono I; Seoud L; Villemure I
    J Mech Behav Biomed Mater; 2023 Jan; 137():105540. PubMed ID: 36327650
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Nature-Inspired Search Method and Custom Waste Object Detection and Classification Model for Smart Waste Bin.
    Agbehadji IE; Abayomi A; Bui KN; Millham RC; Freeman E
    Sensors (Basel); 2022 Aug; 22(16):. PubMed ID: 36015936
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging.
    Fu J; Yang Y; Singhrao K; Ruan D; Chu FI; Low DA; Lewis JH
    Med Phys; 2019 Sep; 46(9):3788-3798. PubMed ID: 31220353
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Phase contrast microscopy asbestos fiber counting performance in the Proficiency Analytical Testing program.
    Schlecht PC; Shulman SA
    Am Ind Hyg Assoc J; 1995 May; 56(5):480-9. PubMed ID: 7754978
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Methodologies for determining the sources, characteristics, distribution, and abundance of asbestiform and nonasbestiform amphibole and serpentine in ambient air and water.
    Wylie AG; Candela PA
    J Toxicol Environ Health B Crit Rev; 2015; 18(1):1-42. PubMed ID: 25825806
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A meta-analysis of asbestos-related cancer risk that addresses fiber size and mineral type.
    Berman DW; Crump KS
    Crit Rev Toxicol; 2008; 38 Suppl 1():49-73. PubMed ID: 18686078
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Development of an automated asbestos counting software based on fluorescence microscopy.
    Alexandrov M; Ichida E; Nishimura T; Aoki K; Ishida T; Hirota R; Ikeda T; Kawasaki T; Kuroda A
    Environ Monit Assess; 2015 Jan; 187(1):4166. PubMed ID: 25467412
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Assessment of airborne asbestos exposure during the servicing and handling of automobile asbestos-containing gaskets.
    Blake CL; Dotson GS; Harbison RD
    Regul Toxicol Pharmacol; 2006 Jul; 45(2):214-22. PubMed ID: 16730109
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Liquid-phase sample preparation method for real-time monitoring of airborne asbestos fibers by dual-mode high-throughput microscopy.
    Cho MO; Kim JK; Han H; Lee J
    Annu Int Conf IEEE Eng Med Biol Soc; 2013; 2013():5517-20. PubMed ID: 24110986
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Lung asbestos fiber burden analysis: effects of the counting rules for legal medicine evaluations.
    Somigliana AB; Barbieri PG; Cavallo A; Colombo R; Consonni D; Mirabelli D
    Inhal Toxicol; 2023; 35(11-12):300-307. PubMed ID: 37995092
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.