BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

909 related articles for article (PubMed ID: 12607839)

  • 1. Automated melanoma detection: multispectral imaging and neural network approach for classification.
    Tomatis S; Bono A; Bartoli C; Carrara M; Lualdi M; Tragni G; Marchesini R
    Med Phys; 2003 Feb; 30(2):212-21. PubMed ID: 12607839
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Automated melanoma detection with a novel multispectral imaging system: results of a prospective study.
    Tomatis S; Carrara M; Bono A; Bartoli C; Lualdi M; Tragni G; Colombo A; Marchesini R
    Phys Med Biol; 2005 Apr; 50(8):1675-87. PubMed ID: 15815089
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Multispectral imaging and artificial neural network: mimicking the management decision of the clinician facing pigmented skin lesions.
    Carrara M; Bono A; Bartoli C; Colombo A; Lualdi M; Moglia D; Santoro N; Tolomio E; Tomatis S; Tragni G; Santinami M; Marchesini R
    Phys Med Biol; 2007 May; 52(9):2599-613. PubMed ID: 17440255
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Multispectral imaging approach in the diagnosis of cutaneous melanoma: potentiality and limits.
    Farina B; Bartoli C; Bono A; Colombo A; Lualdi M; Tragni G; Marchesini R
    Phys Med Biol; 2000 May; 45(5):1243-54. PubMed ID: 10843103
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Predictive power of irregular border shapes for malignant melanomas.
    Lee TK; Claridge E
    Skin Res Technol; 2005 Feb; 11(1):1-8. PubMed ID: 15691253
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A methodological approach to the classification of dermoscopy images.
    Celebi ME; Kingravi HA; Uddin B; Iyatomi H; Aslandogan YA; Stoecker WV; Moss RH
    Comput Med Imaging Graph; 2007 Sep; 31(6):362-73. PubMed ID: 17387001
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Computer-based classification of dermoscopy images of melanocytic lesions on acral volar skin.
    Iyatomi H; Oka H; Celebi ME; Ogawa K; Argenziano G; Soyer HP; Koga H; Saida T; Ohara K; Tanaka M
    J Invest Dermatol; 2008 Aug; 128(8):2049-54. PubMed ID: 18323788
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Melanoma detection. A prospective study comparing diagnosis with the naked eye, dermatoscopy and telespectrophotometry.
    Bono A; Bartoli C; Cascinelli N; Lualdi M; Maurichi A; Moglia D; Tragni G; Tomatis S; Marchesini R
    Dermatology; 2002; 205(4):362-6. PubMed ID: 12444332
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A novel cumulative level difference mean based GLDM and modified ABCD features ranked using eigenvector centrality approach for four skin lesion types classification.
    Wahba MA; Ashour AS; Guo Y; Napoleon SA; Elnaby MMA
    Comput Methods Programs Biomed; 2018 Oct; 165():163-174. PubMed ID: 30337071
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The ABCD system of melanoma detection: a spectrophotometric analysis of the Asymmetry, Border, Color, and Dimension.
    Bono A; Tomatis S; Bartoli C; Tragni G; Radaelli G; Maurichi A; Marchesini R
    Cancer; 1999 Jan; 85(1):72-7. PubMed ID: 9921976
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Combination of features from skin pattern and ABCD analysis for lesion classification.
    She Z; Liu Y; Damatoa A
    Skin Res Technol; 2007 Feb; 13(1):25-33. PubMed ID: 17250529
    [TBL] [Abstract][Full Text] [Related]  

  • 12. A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions.
    Grana C; Pellacani G; Cucchiara R; Seidenari S
    IEEE Trans Med Imaging; 2003 Aug; 22(8):959-64. PubMed ID: 12906250
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Determination of border irregularity in dermoscopic color images of pigmented skin lesions.
    Jaworek-Korjakowska J; Tadeusiewicz R
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():6459-62. PubMed ID: 25571475
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network.
    Binder M; Kittler H; Seeber A; Steiner A; Pehamberger H; Wolff K
    Melanoma Res; 1998 Jun; 8(3):261-6. PubMed ID: 9664148
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Enhancement of lesion classification using divergence, curl and curvature of skin pattern.
    She Z; Duller AW; Fish PJ
    Skin Res Technol; 2004 Nov; 10(4):222-30. PubMed ID: 15479445
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Validation of a Skin-Lesion Image-Matching Algorithm Based on Computer Vision Technology.
    Chen RH; Snorrason M; Enger SM; Mostafa E; Ko JM; Aoki V; Bowling J
    Telemed J E Health; 2016 Jan; 22(1):45-50. PubMed ID: 26218353
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit: a preliminary report.
    Manousaki AG; Manios AG; Tsompanaki EI; Panayiotides JG; Tsiftsis DD; Kostaki AK; Tosca AD
    Int J Dermatol; 2006 Apr; 45(4):402-10. PubMed ID: 16650167
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features.
    Joo S; Yang YS; Moon WK; Kim HC
    IEEE Trans Med Imaging; 2004 Oct; 23(10):1292-300. PubMed ID: 15493696
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Computer-aided classification of melanocytic lesions using dermoscopic images.
    Ferris LK; Harkes JA; Gilbert B; Winger DG; Golubets K; Akilov O; Satyanarayanan M
    J Am Acad Dermatol; 2015 Nov; 73(5):769-76. PubMed ID: 26386631
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors.
    Jesneck JL; Lo JY; Baker JA
    Radiology; 2007 Aug; 244(2):390-8. PubMed ID: 17562812
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 46.