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- 2019
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Tidskriftsartikel (refereegranskat)
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- Haenssle, H A, et al.
(författare)
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Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.
- 2018
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Ingår i: Annals of Oncology. - : Elsevier BV. - 1569-8041 .- 0923-7534. ; 29:8, s. 1836-1842
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Tidskriftsartikel (refereegranskat)abstract
- 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/).
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- Opitz, Andreas, et al.
(författare)
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Organic heterojunctions : Contact-induced molecular reorientation, interface states, and charge redistribution
- 2016
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- We reveal the rather complex interplay of contact-induced re-orientation and interfacial electronic structure-in the presence of Fermi-level pinning-at prototypical molecular heterojunctions comprising copper phthalocyanine (H16CuPc) and its perfluorinated analogue (F16CuPc), by employing ultraviolet photoelectron and X-ray absorption spectroscopy. For both layer sequences, we find that Fermi-level (E-F) pinning of the first layer on the conductive polymer substrate modifies the work function encountered by the second layer such that it also becomes E-F-pinned, however, at the interface towards the first molecular layer. This results in a charge transfer accompanied by a sheet charge density at the organic/organic interface. While molecules in the bulk of the films exhibit upright orientation, contact formation at the heterojunction results in an interfacial bilayer with lying and co-facial orientation. This interfacial layer is not EF-pinned, but provides for an additional density of states at the interface that is not present in the bulk. With reliable knowledge of the organic heterojunction's electronic structure we can explain the poor performance of these in photovoltaic cells as well as their valuable function as charge generation layer in electronic devices.
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| 18. |
- Sinz, Christoph, et al.
(författare)
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Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin.
- 2017
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Ingår i: Journal of the American Academy of Dermatology. - : Elsevier BV. - 1097-6787 .- 0190-9622. ; 77:6, s. 1100-1109
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Tidskriftsartikel (refereegranskat)abstract
- Nonpigmented skin cancer is common, and diagnosis with the unaided eye is error prone.To investigate whether dermatoscopy improves the diagnostic accuracy for nonpigmented (amelanotic) cutaneous neoplasms.We collected a sample of 2072 benign and malignant neoplastic lesions and inflammatory conditions and presented close-up images taken with and without dermatoscopy to 95 examiners with different levels of experience.The area under the curve was significantly higher with than without dermatoscopy (0.68 vs 0.64, P<.001). Among 51 possible diagnoses, the correct diagnosis was selected in 33.1% of cases with and 26.4% of cases without dermatoscopy (P<.001). For experts, the frequencies of correct specific diagnoses of a malignant lesion improved from 40.2% without to 51.3% with dermatoscopy. For all malignant neoplasms combined, the frequencies of appropriate management strategies increased from 78.1% without to 82.5% with dermatoscopy.The study deviated from a real-life clinical setting and was potentially affected by verification and selection bias.Dermatoscopy improves the diagnosis and management of nonpigmented skin cancer and should be used as an adjunct to examination with the unaided eye.
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| 19. |
- Tschandl, Philipp, et al.
(författare)
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Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks.
- 2019
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Ingår i: JAMA dermatology. - : American Medical Association (AMA). - 2168-6084 .- 2168-6068. ; 55:1, s. 58-65
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Tidskriftsartikel (refereegranskat)abstract
- Convolutional neural networks (CNNs) achieve expert-level accuracy in the diagnosis of pigmented melanocytic lesions. However, the most common types of skin cancer are nonpigmented and nonmelanocytic, and are more difficult to diagnose.To compare the accuracy of a CNN-based classifier with that of physicians with different levels of experience.A CNN-based classification model was trained on 7895 dermoscopic and 5829 close-up images of lesions excised at a primary skin cancer clinic between January 1, 2008, and July 13, 2017, for a combined evaluation of both imaging methods. The combined CNN (cCNN) was tested on a set of 2072 unknown cases and compared with results from 95 human raters who were medical personnel, including 62 board-certified dermatologists, with different experience in dermoscopy.The proportions of correct specific diagnoses and the accuracy to differentiate between benign and malignant lesions measured as an area under the receiver operating characteristic curve served as main outcome measures.Among 95 human raters (51.6% female; mean age, 43.4 years; 95% CI, 41.0-45.7 years), the participants were divided into 3 groups (according to years of experience with dermoscopy): beginner raters (<3 years), intermediate raters (3-10 years), or expert raters (>10 years). The area under the receiver operating characteristic curve of the trained cCNN was higher than human ratings (0.742; 95% CI, 0.729-0.755 vs 0.695; 95% CI, 0.676-0.713; P<.001). The specificity was fixed at the mean level of human raters (51.3%), and therefore the sensitivity of the cCNN (80.5%; 95% CI, 79.0%-82.1%) was higher than that of human raters (77.6%; 95% CI, 74.7%-80.5%). The cCNN achieved a higher percentage of correct specific diagnoses compared with human raters (37.6%; 95% CI, 36.6%-38.4% vs 33.5%; 95% CI, 31.5%-35.6%; P=.001) but not compared with experts (37.3%; 95% CI, 35.7%-38.8% vs 40.0%; 95% CI, 37.0%-43.0%; P=.18).Neural networks are able to classify dermoscopic and close-up images of nonpigmented lesions as accurately as human experts in an experimental setting.
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| 20. |
- Aad, G., et al.
(författare)
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- 2015
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Ingår i: Physical Review C (Nuclear Physics). - 0556-2813 .- 1089-490X. ; 92:3
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Tidskriftsartikel (refereegranskat)
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| 21. |
- Aad, G., et al.
(författare)
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- 2015
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Ingår i: Physical Review C (Nuclear Physics). - 0556-2813 .- 1089-490X. ; 92:3
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| 22. |
- Aad, G., et al.
(författare)
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- 2015
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Tidskriftsartikel (refereegranskat)
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| 23. |
- Aad, G., et al.
(författare)
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- 2015
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Ingår i: Physical Review D (Particles, Fields, Gravitation and Cosmology). - 1550-2368 .- 1550-7998. ; 91:11, s. 112011-
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Tidskriftsartikel (refereegranskat)
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| 24. |
- Aad, G., et al.
(författare)
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- 2015
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Ingår i: Physical Review Letters. - 1079-7114 .- 0031-9007. ; 115:9
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Tidskriftsartikel (refereegranskat)
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| 25. |
- Aad, G., et al.
(författare)
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- 2015
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Ingår i: Journal of High Energy Physics. - : Springer-Verlag New York. - 1029-8479 .- 1126-6708. ; :9
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Tidskriftsartikel (refereegranskat)
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