BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

517 related articles for article (PubMed ID: 32751349)

  • 21. Classification of skin lesions using transfer learning and augmentation with Alex-net.
    Hosny KM; Kassem MA; Foaud MM
    PLoS One; 2019; 14(5):e0217293. PubMed ID: 31112591
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Observational study investigating the level of support from a convolutional neural network in face and scalp lesions deemed diagnostically 'unclear' by dermatologists.
    Kommoss KS; Winkler JK; Mueller-Christmann C; Bardehle F; Toberer F; Stolz W; Kraenke T; Hofmann-Wellenhof R; Blum A; Enk A; Rosenberger A; Haenssle HA
    Eur J Cancer; 2023 May; 185():53-60. PubMed ID: 36963352
    [TBL] [Abstract][Full Text] [Related]  

  • 23. DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images.
    Nasiri S; Helsper J; Jung M; Fathi M
    BMC Bioinformatics; 2020 Mar; 21(Suppl 2):84. PubMed ID: 32164530
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.
    Fink C; Blum A; Buhl T; Mitteldorf C; Hofmann-Wellenhof R; Deinlein T; Stolz W; Trennheuser L; Cussigh C; Deltgen D; Winkler JK; Toberer F; Enk A; Rosenberger A; Haenssle HA
    J Eur Acad Dermatol Venereol; 2020 Jun; 34(6):1355-1361. PubMed ID: 31856342
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers.
    Huang HW; Hsu BW; Lee CH; Tseng VS
    J Dermatol; 2021 Mar; 48(3):310-316. PubMed ID: 33211346
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Convolutional neural network assistance significantly improves dermatologists' diagnosis of cutaneous tumours using clinical images.
    Ba W; Wu H; Chen WW; Wang SH; Zhang ZY; Wei XJ; Wang WJ; Yang L; Zhou DM; Zhuang YX; Zhong Q; Song ZG; Li CX
    Eur J Cancer; 2022 Jul; 169():156-165. PubMed ID: 35569282
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Robustness of convolutional neural networks in recognition of pigmented skin lesions.
    Maron RC; Haggenmüller S; von Kalle C; Utikal JS; Meier F; Gellrich FF; Hauschild A; French LE; Schlaak M; Ghoreschi K; Kutzner H; Heppt MV; Haferkamp S; Sondermann W; Schadendorf D; Schilling B; Hekler A; Krieghoff-Henning E; Kather JN; Fröhling S; Lipka DB; Brinker TJ
    Eur J Cancer; 2021 Mar; 145():81-91. PubMed ID: 33423009
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap.
    Minagawa A; Koga H; Sano T; Matsunaga K; Teshima Y; Hamada A; Houjou Y; Okuyama R
    J Dermatol; 2021 Feb; 48(2):232-236. PubMed ID: 33063398
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions.
    Haenssle HA; Fink C; Toberer F; Winkler J; Stolz W; Deinlein T; Hofmann-Wellenhof R; Lallas A; Emmert S; Buhl T; Zutt M; Blum A; Abassi MS; Thomas L; Tromme I; Tschandl P; Enk A; Rosenberger A;
    Ann Oncol; 2020 Jan; 31(1):137-143. PubMed ID: 31912788
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Skin lesion classification with ensembles of deep convolutional neural networks.
    Harangi B
    J Biomed Inform; 2018 Oct; 86():25-32. PubMed ID: 30103029
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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]  

  • 32. A convolutional neural network trained with dermoscopic images of psoriasis performed on par with 230 dermatologists.
    Yang Y; Wang J; Xie F; Liu J; Shu C; Wang Y; Zheng Y; Zhang H
    Comput Biol Med; 2021 Dec; 139():104924. PubMed ID: 34688173
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.
    Haenssle HA; Fink C; Schneiderbauer R; Toberer F; Buhl T; Blum A; Kalloo A; Hassen ABH; Thomas L; Enk A; Uhlmann L; ; Alt C; Arenbergerova M; Bakos R; Baltzer A; Bertlich I; Blum A; Bokor-Billmann T; Bowling J; Braghiroli N; Braun R; Buder-Bakhaya K; Buhl T; Cabo H; Cabrijan L; Cevic N; Classen A; Deltgen D; Fink C; Georgieva I; Hakim-Meibodi LE; Hanner S; Hartmann F; Hartmann J; Haus G; Hoxha E; Karls R; Koga H; Kreusch J; Lallas A; Majenka P; Marghoob A; Massone C; Mekokishvili L; Mestel D; Meyer V; Neuberger A; Nielsen K; Oliviero M; Pampena R; Paoli J; Pawlik E; Rao B; Rendon A; Russo T; Sadek A; Samhaber K; Schneiderbauer R; Schweizer A; Toberer F; Trennheuser L; Vlahova L; Wald A; Winkler J; Wölbing P; Zalaudek I
    Ann Oncol; 2018 Aug; 29(8):1836-1842. PubMed ID: 29846502
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition.
    Winkler JK; Sies K; Fink C; Toberer F; Enk A; Abassi MS; Fuchs T; Haenssle HA
    Eur J Cancer; 2021 Mar; 145():146-154. PubMed ID: 33465706
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study.
    Han SS; Moon IJ; Kim SH; Na JI; Kim MS; Park GH; Park I; Kim K; Lim W; Lee JH; Chang SE
    PLoS Med; 2020 Nov; 17(11):e1003381. PubMed ID: 33237903
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.
    Goessinger EV; Cerminara SE; Mueller AM; Gottfrois P; Huber S; Amaral M; Wenz F; Kostner L; Weiss L; Kunz M; Maul JT; Wespi S; Broman E; Kaufmann S; Patpanathapillai V; Treyer I; Navarini AA; Maul LV
    J Eur Acad Dermatol Venereol; 2024 May; 38(5):945-953. PubMed ID: 38158385
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification.
    Al-Masni MA; Kim DH; Kim TS
    Comput Methods Programs Biomed; 2020 Jul; 190():105351. PubMed ID: 32028084
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Melanoma diagnosis using deep learning techniques on dermatoscopic images.
    Jojoa Acosta MF; Caballero Tovar LY; Garcia-Zapirain MB; Percybrooks WS
    BMC Med Imaging; 2021 Jan; 21(1):6. PubMed ID: 33407213
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.
    Rios-Duarte JA; Diaz-Valencia AC; Combariza G; Feles M; Peña-Silva RA
    Skin Res Technol; 2024 May; 30(5):e13607. PubMed ID: 38742379
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.
    Marchetti MA; Codella NCF; Dusza SW; Gutman DA; Helba B; Kalloo A; Mishra N; Carrera C; Celebi ME; DeFazio JL; Jaimes N; Marghoob AA; Quigley E; Scope A; Yélamos O; Halpern AC;
    J Am Acad Dermatol; 2018 Feb; 78(2):270-277.e1. PubMed ID: 28969863
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 26.