BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

312 related articles for article (PubMed ID: 33063398)

  • 1. 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]  

  • 2. Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population.
    Wang SQ; Zhang XY; Liu J; Tao C; Zhu CY; Shu C; Xu T; Jin HZ
    Chin Med J (Engl); 2020 Sep; 133(17):2027-2036. PubMed ID: 32826613
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.
    Maron RC; Weichenthal M; Utikal JS; Hekler A; Berking C; Hauschild A; Enk AH; Haferkamp S; Klode J; Schadendorf D; Jansen P; Holland-Letz T; Schilling B; von Kalle C; Fröhling S; Gaiser MR; Hartmann D; Gesierich A; Kähler KC; Wehkamp U; Karoglan A; Bär C; Brinker TJ;
    Eur J Cancer; 2019 Sep; 119():57-65. PubMed ID: 31419752
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images.
    Iqbal I; Younus M; Walayat K; Kakar MU; Ma J
    Comput Med Imaging Graph; 2021 Mar; 88():101843. PubMed ID: 33445062
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.
    Maron RC; Utikal JS; Hekler A; Hauschild A; Sattler E; Sondermann W; Haferkamp S; Schilling B; Heppt MV; Jansen P; Reinholz M; Franklin C; Schmitt L; Hartmann D; Krieghoff-Henning E; Schmitt M; Weichenthal M; von Kalle C; Fröhling S; Brinker TJ
    J Med Internet Res; 2020 Sep; 22(9):e18091. PubMed ID: 32915161
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.
    Marchetti MA; Liopyris K; Dusza SW; Codella NCF; Gutman DA; Helba B; Kalloo A; Halpern AC;
    J Am Acad Dermatol; 2020 Mar; 82(3):622-627. PubMed ID: 31306724
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.
    Brinker TJ; Hekler A; Enk AH; Klode J; Hauschild A; Berking C; Schilling B; Haferkamp S; Schadendorf D; Fröhling S; Utikal JS; von Kalle C;
    Eur J Cancer; 2019 Apr; 111():148-154. PubMed ID: 30852421
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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]  

  • 10. A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi.
    Tognetti L; Bonechi S; Andreini P; Bianchini M; Scarselli F; Cevenini G; Moscarella E; Farnetani F; Longo C; Lallas A; Carrera C; Puig S; Tiodorovic D; Perrot JL; Pellacani G; Argenziano G; Cinotti E; Cataldo G; Balistreri A; Mecocci A; Gori M; Rubegni P; Cartocci A
    J Dermatol Sci; 2021 Feb; 101(2):115-122. PubMed ID: 33358096
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task.
    Brinker TJ; Hekler A; Enk AH; Klode J; Hauschild A; Berking C; Schilling B; Haferkamp S; Schadendorf D; Holland-Letz T; Utikal JS; von Kalle C;
    Eur J Cancer; 2019 May; 113():47-54. PubMed ID: 30981091
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. 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]  

  • 14. Deep neural networks are superior to dermatologists in melanoma image classification.
    Brinker TJ; Hekler A; Enk AH; Berking C; Haferkamp S; Hauschild A; Weichenthal M; Klode J; Schadendorf D; Holland-Letz T; von Kalle C; Fröhling S; Schilling B; Utikal JS
    Eur J Cancer; 2019 Sep; 119():11-17. PubMed ID: 31401469
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Skin lesions of face and scalp - Classification by a market-approved convolutional neural network in comparison with 64 dermatologists.
    Haenssle HA; Winkler JK; Fink C; Toberer F; Enk A; Stolz W; Deinlein T; Hofmann-Wellenhof R; Kittler H; Tschandl P; Rosendahl C; Lallas A; Blum A; Abassi MS; Thomas L; Tromme I; Rosenberger A;
    Eur J Cancer; 2021 Feb; 144():192-199. PubMed ID: 33370644
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images.
    Kaur R; GholamHosseini H; Sinha R; Lindén M
    Sensors (Basel); 2022 Feb; 22(3):. PubMed ID: 35161878
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network.
    Gillstedt M; Mannius L; Paoli J; Dahlén Gyllencreutz J; Fougelberg J; Johansson Backman E; Pakka J; Zaar O; Polesie S
    Acta Derm Venereol; 2022 Oct; 102():adv00790. PubMed ID: 36172695
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Reducing the Impact of Confounding Factors on Skin Cancer Classification via Image Segmentation: Technical Model Study.
    Maron RC; Hekler A; Krieghoff-Henning E; Schmitt M; Schlager JG; Utikal JS; Brinker TJ
    J Med Internet Res; 2021 Mar; 23(3):e21695. PubMed ID: 33764307
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 16.