BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

120 related articles for article (PubMed ID: 30440474)

  • 1. Temporal Detection of Changes in the Vascularity and Concentration of Pigment Structures of a Skin Lesion.
    Dhinagar NJ; Celenk M
    Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():624-627. PubMed ID: 30440474
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Role of In Vivo Reflectance Confocal Microscopy in the Analysis of Melanocytic Lesions.
    Serban ED; Farnetani F; Pellacani G; Constantin MM
    Acta Dermatovenerol Croat; 2018 Apr; 26(1):64-67. PubMed ID: 29782304
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images.
    Kaya S; Bayraktar M; Kockara S; Mete M; Halic T; Field HE; Wong HK
    BMC Bioinformatics; 2016 Oct; 17(Suppl 13):367. PubMed ID: 27766942
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Pigment distribution in melanocytic lesion images: a digital parameter to be employed for computer-aided diagnosis.
    Seidenari S; Pellacani G; Grana C
    Skin Res Technol; 2005 Nov; 11(4):236-41. PubMed ID: 16221139
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Biologically Inspired QuadTree Color Detection in Dermoscopy Images of Melanoma.
    Sabbaghi Mahmouei S; Aldeen M; Stoecker WV; Garnavi R
    IEEE J Biomed Health Inform; 2019 Mar; 23(2):570-577. PubMed ID: 29993590
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Active Contours Based Segmentation and Lesion Periphery Analysis For Characterization of Skin Lesions in Dermoscopy Images.
    Riaz F; Naeem S; Nawaz R; Coimbra MT
    IEEE J Biomed Health Inform; 2019 Mar; 23(2):489-500. PubMed ID: 29993589
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Automatic lesion border selection in dermoscopy images using morphology and color features.
    Mishra NK; Kaur R; Kasmi R; Hagerty JR; LeAnder R; Stanley RJ; Moss RH; Stoecker WV
    Skin Res Technol; 2019 Jul; 25(4):544-552. PubMed ID: 30868667
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Detecting the pigment network in dermoscopy images: a directional approach.
    Barata C; Marques JS; Rozeira J
    Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():5120-3. PubMed ID: 22255491
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Assessment of malignant melanoma lesions using violet-light dermoscopy: A case report.
    Shu J; Yamamoto Y; Aoyama K; Togawa Y; Kishimoto T; Matsue H
    J Dermatol; 2022 Jul; 49(7):710-713. PubMed ID: 35434834
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Melanoma recognition in dermoscopy images using lesion's peripheral region information.
    Tajeddin NZ; Asl BM
    Comput Methods Programs Biomed; 2018 Sep; 163():143-153. PubMed ID: 30119849
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding.
    Garcia-Arroyo JL; Garcia-Zapirain B
    Comput Methods Programs Biomed; 2019 Jan; 168():11-19. PubMed ID: 30527129
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Detection of pigment network in dermoscopy images using supervised machine learning and structural analysis.
    García Arroyo JL; García Zapirain B
    Comput Biol Med; 2014 Jan; 44():144-57. PubMed ID: 24314859
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Automatic detection of blue-white veil and related structures in dermoscopy images.
    Celebi ME; Iyatomi H; Stoecker WV; Moss RH; Rabinovitz HS; Argenziano G; Soyer HP
    Comput Med Imaging Graph; 2008 Dec; 32(8):670-7. PubMed ID: 18804955
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Acral melanoma detection using a convolutional neural network for dermoscopy images.
    Yu C; Yang S; Kim W; Jung J; Chung KY; Lee SW; Oh B
    PLoS One; 2018; 13(3):e0193321. PubMed ID: 29513718
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Melanoma detection by analysis of clinical images using convolutional neural network.
    Nasr-Esfahani E; Samavi S; Karimi N; Soroushmehr SM; Jafari MH; Ward K; Najarian K
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():1373-1376. PubMed ID: 28268581
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A deep bag-of-features model for the classification of melanomas in dermoscopy images.
    Sabbaghi S; Aldeen M; Garnavi R
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():1369-1372. PubMed ID: 28268580
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Digital imaging biomarkers feed machine learning for melanoma screening.
    Gareau DS; Correa da Rosa J; Yagerman S; Carucci JA; Gulati N; Hueto F; DeFazio JL; Suárez-Fariñas M; Marghoob A; Krueger JG
    Exp Dermatol; 2017 Jul; 26(7):615-618. PubMed ID: 27783441
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Digital Dermoscopy Photographs Outperform Handheld Dermoscopy in Melanoma Diagnosis.
    Mintsoulis D; Beecker J
    J Cutan Med Surg; 2016 Nov; 20(6):602-605. PubMed ID: 27270098
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Automated Detection and Segmentation of Vascular Structures of Skin Lesions Seen in Dermoscopy, With an Application to Basal Cell Carcinoma Classification.
    Kharazmi P; AlJasser MI; Lui H; Wang ZJ; Lee TK
    IEEE J Biomed Health Inform; 2017 Nov; 21(6):1675-1684. PubMed ID: 27959832
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Physiological characterization of skin lesion using non-linear random forest regression model.
    Cho DS; Haider S; Amelard R; Wong A; Clausi D
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():3349-52. PubMed ID: 25570708
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.