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Man against machine...
Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.
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- Haenssle, H A (author)
- Heidelberg University
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- Fink, C (author)
- Heidelberg University
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- Schneiderbauer, R (author)
- Heidelberg University
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show more...
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- Toberer, F (author)
- Heidelberg University
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- Buhl, T (author)
- University of Göttingen
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Blum, A (author)
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- Kalloo, A (author)
- Memorial Sloan-Kettering Cancer Center
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- Hassen, A Ben Hadj (author)
- University of Passau
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- Thomas, L (author)
- Claude Bernard University Lyon 1
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- Enk, A (author)
- Heidelberg University
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- Uhlmann, L (author)
- Heidelberg University
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Alt, Christina (author)
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Arenbergerova, Monika (author)
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Bakos, Renato (author)
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Baltzer, Anne (author)
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Bertlich, Ines (author)
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Blum, Andreas (author)
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Bokor-Billmann, Therezia (author)
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Bowling, Jonathan (author)
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Braghiroli, Naira (author)
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Braun, Ralph (author)
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Buder-Bakhaya, Kristina (author)
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Buhl, Timo (author)
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Cabo, Horacio (author)
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Cabrijan, Leo (author)
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Cevic, Naciye (author)
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Classen, Anna (author)
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Deltgen, David (author)
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Fink, Christine (author)
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Georgieva, Ivelina (author)
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Hakim-Meibodi, Lara-Elena (author)
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Hanner, Susanne (author)
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Hartmann, Franziska (author)
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Hartmann, Julia (author)
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Haus, Georg (author)
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Hoxha, Elti (author)
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Karls, Raimonds (author)
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Koga, Hiroshi (author)
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Kreusch, Jürgen (author)
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Lallas, Aimilios (author)
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Majenka, Pawel (author)
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Marghoob, Ash (author)
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Massone, Cesare (author)
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Mekokishvili, Lali (author)
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Mestel, Dominik (author)
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Meyer, Volker (author)
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Neuberger, Anna (author)
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- Nielsen, Kari (author)
- Lund University,Lunds universitet,Dermatologi och venereologi, Lund,Sektion III,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Dermatology and Venereology (Lund),Section III,Department of Clinical Sciences, Lund,Faculty of Medicine
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Oliviero, Margaret (author)
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Pampena, Riccardo (author)
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- Paoli, John, 1975 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för dermatologi och venereologi,Institute of Clinical Sciences, Department of Dermatology and Venereology
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Pawlik, Erika (author)
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Rao, Barbar (author)
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Rendon, Adriana (author)
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Russo, Teresa (author)
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Sadek, Ahmed (author)
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Samhaber, Kinga (author)
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Schneiderbauer, Roland (author)
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Schweizer, Anissa (author)
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Toberer, Ferdinand (author)
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Trennheuser, Lukas (author)
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Vlahova, Lyobomira (author)
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Wald, Alexander (author)
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Winkler, Julia (author)
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Wölbing, Priscila (author)
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Zalaudek, Iris (author)
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(creator_code:org_t)
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- Elsevier BV, 2018
- 2018
- English.
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In: Annals of Oncology. - : Elsevier BV. - 1569-8041 .- 0923-7534. ; 29:8, s. 1836-1842
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http://dx.doi.org/10...
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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https://lup.lub.lu.s...
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Abstract
Subject headings
Close
- Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking.Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge.In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P=0.19) and specificity to 75.7% (±11.7%, P<0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P<0.01) and level-II (75.7%, P<0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P<0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge.For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification.This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Dermatologi och venereologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Dermatology and Venereal Diseases (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Keyword
- Clinical Competence
- Cross-Sectional Studies
- Deep Learning
- Dermatologists
- statistics & numerical data
- Dermoscopy
- Humans
- Image Processing
- Computer-Assisted
- methods
- statistics & numerical data
- International Cooperation
- Melanoma
- diagnostic imaging
- ROC Curve
- Retrospective Studies
- Skin
- diagnostic imaging
- Skin Neoplasms
- diagnostic imaging
- melanoma
- melanocytic nevi
- dermoscopy
- deep learning convolutional neural network
- computer algorithm
- automated melonoma detection
Publication and Content Type
- ref (subject category)
- art (subject category)
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To the university's database
- By the author/editor
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Haenssle, H A
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Fink, C
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Schneiderbauer, ...
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Toberer, F
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Buhl, T
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Blum, A
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show more...
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Kalloo, A
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Hassen, A Ben Ha ...
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Thomas, L
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Enk, A
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Uhlmann, L
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Alt, Christina
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Arenbergerova, M ...
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Bakos, Renato
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Baltzer, Anne
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Bertlich, Ines
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Blum, Andreas
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Bokor-Billmann, ...
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Bowling, Jonatha ...
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Braghiroli, Nair ...
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Braun, Ralph
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Buder-Bakhaya, K ...
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Buhl, Timo
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Cabo, Horacio
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Cabrijan, Leo
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Cevic, Naciye
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Classen, Anna
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Deltgen, David
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Fink, Christine
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Georgieva, Iveli ...
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Hakim-Meibodi, L ...
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Hanner, Susanne
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Hartmann, Franzi ...
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Hartmann, Julia
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Haus, Georg
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Hoxha, Elti
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Karls, Raimonds
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Koga, Hiroshi
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Kreusch, Jürgen
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Lallas, Aimilios
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Majenka, Pawel
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Marghoob, Ash
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Massone, Cesare
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Mekokishvili, La ...
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Mestel, Dominik
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Meyer, Volker
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Neuberger, Anna
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Nielsen, Kari
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Oliviero, Margar ...
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Pampena, Riccard ...
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Paoli, John, 197 ...
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Pawlik, Erika
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Rao, Barbar
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Rendon, Adriana
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Russo, Teresa
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Sadek, Ahmed
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Samhaber, Kinga
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Schneiderbauer, ...
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Schweizer, Aniss ...
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Toberer, Ferdina ...
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Trennheuser, Luk ...
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Vlahova, Lyobomi ...
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Wald, Alexander
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Winkler, Julia
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Wölbing, Priscil ...
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Zalaudek, Iris
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Dermatology and ...
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Radiology Nuclea ...
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Clinical Medicin ...
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and Cancer and Oncol ...
- Articles in the publication
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Annals of Oncolo ...
- By the university
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University of Gothenburg
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Lund University