593 related articles for article (PubMed ID: 31401469)
1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. Superior skin cancer classification by the combination of human and artificial intelligence.
Hekler A; Utikal JS; Enk AH; Hauschild A; Weichenthal M; Maron RC; Berking C; Haferkamp S; Klode J; Schadendorf D; Schilling B; Holland-Letz T; Izar B; von Kalle C; Fröhling S; Brinker TJ;
Eur J Cancer; 2019 Oct; 120():114-121. PubMed ID: 31518967
[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. Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.
Hekler A; Utikal JS; Enk AH; Solass W; Schmitt M; Klode J; Schadendorf D; Sondermann W; Franklin C; Bestvater F; Flaig MJ; Krahl D; von Kalle C; Fröhling S; Brinker TJ
Eur J Cancer; 2019 Sep; 118():91-96. PubMed ID: 31325876
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. Dermatologist-level classification of skin cancer with deep neural networks.
Esteva A; Kuprel B; Novoa RA; Ko J; Swetter SM; Blau HM; Thrun S
Nature; 2017 Feb; 542(7639):115-118. PubMed ID: 28117445
[TBL] [Abstract][Full Text] [Related]
12. Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening?
Cerminara SE; Cheng P; Kostner L; Huber S; Kunz M; Maul JT; Böhm JS; Dettwiler CF; Geser A; Jakopović C; Stoffel LM; Peter JK; Levesque M; Navarini AA; Maul LV
Eur J Cancer; 2023 Sep; 190():112954. PubMed ID: 37453242
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Pathologist-level classification of histopathological melanoma images with deep neural networks.
Hekler A; Utikal JS; Enk AH; Berking C; Klode J; Schadendorf D; Jansen P; Franklin C; Holland-Letz T; Krahl D; von Kalle C; Fröhling S; Brinker TJ
Eur J Cancer; 2019 Jul; 115():79-83. PubMed ID: 31129383
[TBL] [Abstract][Full Text] [Related]
15. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.
Haggenmüller S; Maron RC; Hekler A; Utikal JS; Barata C; Barnhill RL; Beltraminelli H; Berking C; Betz-Stablein B; Blum A; Braun SA; Carr R; Combalia M; Fernandez-Figueras MT; Ferrara G; Fraitag S; French LE; Gellrich FF; Ghoreschi K; Goebeler M; Guitera P; Haenssle HA; Haferkamp S; Heinzerling L; Heppt MV; Hilke FJ; Hobelsberger S; Krahl D; Kutzner H; Lallas A; Liopyris K; Llamas-Velasco M; Malvehy J; Meier F; Müller CSL; Navarini AA; Navarrete-Dechent C; Perasole A; Poch G; Podlipnik S; Requena L; Rotemberg VM; Saggini A; Sangueza OP; Santonja C; Schadendorf D; Schilling B; Schlaak M; Schlager JG; Sergon M; Sondermann W; Soyer HP; Starz H; Stolz W; Vale E; Weyers W; Zink A; Krieghoff-Henning E; Kather JN; von Kalle C; Lipka DB; Fröhling S; Hauschild A; Kittler H; Brinker TJ
Eur J Cancer; 2021 Oct; 156():202-216. PubMed ID: 34509059
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions.
Brinker TJ; Hekler A; Enk AH; von Kalle C
PLoS One; 2019; 14(6):e0218713. PubMed ID: 31233565
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark.
Brinker TJ; Hekler A; Hauschild A; Berking C; Schilling B; Enk AH; Haferkamp S; Karoglan A; von Kalle C; Weichenthal M; Sattler E; Schadendorf D; Gaiser MR; Klode J; Utikal JS
Eur J Cancer; 2019 Apr; 111():30-37. PubMed ID: 30802784
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
