BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

266 related articles for article (PubMed ID: 32243882)

  • 21. 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]  

  • 22. Dermoscopy Proficiency Expectations for US Dermatology Resident Physicians: Results of a Modified Delphi Survey of Pigmented Lesion Experts.
    Fried LJ; Tan A; Berry EG; Braun RP; Curiel-Lewandrowski C; Curtis J; Ferris LK; Hartman RI; Jaimes N; Kawaoka JC; Kim CC; Lallas A; Leachman SA; Levin A; Lucey P; Marchetti MA; Marghoob AA; Miller D; Nelson KC; Prodanovic E; Seiverling EV; Swetter SM; Savory SA; Usatine RP; Wei ML; Polsky D; Stein JA; Liebman TN
    JAMA Dermatol; 2021 Feb; 157(2):189-197. PubMed ID: 33404623
    [TBL] [Abstract][Full Text] [Related]  

  • 23. 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]  

  • 24. Artificial Intelligence in Dermatology: A Primer.
    Young AT; Xiong M; Pfau J; Keiser MJ; Wei ML
    J Invest Dermatol; 2020 Aug; 140(8):1504-1512. PubMed ID: 32229141
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study.
    Faes L; Wagner SK; Fu DJ; Liu X; Korot E; Ledsam JR; Back T; Chopra R; Pontikos N; Kern C; Moraes G; Schmid MK; Sim D; Balaskas K; Bachmann LM; Denniston AK; Keane PA
    Lancet Digit Health; 2019 Sep; 1(5):e232-e242. PubMed ID: 33323271
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.
    Premaladha J; Ravichandran KS
    J Med Syst; 2016 Apr; 40(4):96. PubMed ID: 26872778
    [TBL] [Abstract][Full Text] [Related]  

  • 27. 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]  

  • 28. AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function.
    Pham TC; Luong CM; Hoang VD; Doucet A
    Sci Rep; 2021 Sep; 11(1):17485. PubMed ID: 34471174
    [TBL] [Abstract][Full Text] [Related]  

  • 29. 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]  

  • 30. Past and present of computer-assisted dermoscopic diagnosis: performance of a conventional image analyser versus a convolutional neural network in a prospective data set of 1,981 skin lesions.
    Sies K; Winkler JK; Fink C; Bardehle F; Toberer F; Buhl T; Enk A; Blum A; Rosenberger A; Haenssle HA
    Eur J Cancer; 2020 Aug; 135():39-46. PubMed ID: 32534243
    [TBL] [Abstract][Full Text] [Related]  

  • 31. 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]  

  • 32. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.
    Han SS; Park GH; Lim W; Kim MS; Na JI; Park I; Chang SE
    PLoS One; 2018; 13(1):e0191493. PubMed ID: 29352285
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Multimodal skin lesion classification using deep learning.
    Yap J; Yolland W; Tschandl P
    Exp Dermatol; 2018 Nov; 27(11):1261-1267. PubMed ID: 30187575
    [TBL] [Abstract][Full Text] [Related]  

  • 34. The Role of Color and Morphologic Characteristics in Dermoscopic Diagnosis.
    Bajaj S; Marchetti MA; Navarrete-Dechent C; Dusza SW; Kose K; Marghoob AA
    JAMA Dermatol; 2016 Jun; 152(6):676-82. PubMed ID: 27007917
    [TBL] [Abstract][Full Text] [Related]  

  • 35. The feasibility of using manual segmentation in a multifeature computer-aided diagnosis system for classification of skin lesions: a retrospective comparative study.
    Chang WY; Huang A; Chen YC; Lin CW; Tsai J; Yang CK; Huang YT; Wu YF; Chen GS
    BMJ Open; 2015 May; 5(4):e007823. PubMed ID: 25941190
    [TBL] [Abstract][Full Text] [Related]  

  • 36. 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]  

  • 37. The degradation of performance of a state-of-the-art skin image classifier when applied to patient-driven internet search.
    Han SS; Navarrete-Dechent C; Liopyris K; Kim MS; Park GH; Woo SS; Park J; Shin JW; Kim BR; Kim MJ; Donoso F; Villanueva F; Ramirez C; Chang SE; Halpern A; Kim SH; Na JI
    Sci Rep; 2022 Sep; 12(1):16260. PubMed ID: 36171272
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Cell phone usefulness to improve the skin cancer screening: preliminary results and critical analysis of mobile app development.
    Silveira CEG; Carcano C; Mauad EC; Faleiros H; Longatto-Filho A
    Rural Remote Health; 2019 Jan; 19(1):4895. PubMed ID: 30673294
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer-aided analysis of data from pigmented skin lesions using digital dermoscopy.
    Hoffmann K; Gambichler T; Rick A; Kreutz M; Anschuetz M; Grünendick T; Orlikov A; Gehlen S; Perotti R; Andreassi L; Newton Bishop J; Césarini JP; Fischer T; Frosch PJ; Lindskov R; Mackie R; Nashan D; Sommer A; Neumann M; Ortonne JP; Bahadoran P; Penas PF; Zoras U; Altmeyer P
    Br J Dermatol; 2003 Oct; 149(4):801-9. PubMed ID: 14616373
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Computer-aided diagnosis of skin lesions using conventional digital photography: a reliability and feasibility study.
    Chang WY; Huang A; Yang CY; Lee CH; Chen YC; Wu TY; Chen GS
    PLoS One; 2013; 8(11):e76212. PubMed ID: 24223698
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 14.