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We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis.
Skin conditions and related need for medical care among persons 1–74 years
NHANES · 1978
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Comparison of dermatologic diagnoses by primary care practitioners and dermatologists. a review of the literature
D. Federman, J.Concato, and R. Kirsner · 1999
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The burden of skin diseases: 2004 a joint project of the american academy of dermatology association and the society for investigative dermatology
D. Bickers, H. Lim, and D. Margolis · 2006
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Digital dermoscopic monitoring of atypical nevi in patients at risk for melanoma
S. R. Fuller, G. M. Bowen, B. Tanner, S. R. Florell, and D. Grossman · 2007
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Skin diseases get misdiagnosed in primary care
E. E. Goldman · 2007
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Burden of skin diseases
M. Basra and M. Shahrukh · 2009
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Skin conditions in the uk: A health needs assessment
J.K.Scholfield, D. Grindlay, and H. Williams · 2009
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Implementation and benchmarking of perceptual image hash functions
C. Zauner · 2010
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The assessment of dermatological needs in resource-poor regions
R. Hay and L. Fuller · 2012
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Most common dermatologic conditions encountered by dermatologists and nondermatologists
E. Wilmer, C. Gustafson, C. Ahn, S. Davis, S. Feldman, and W. Huang · 2014
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Capturing long-tail distributions of object subcategories
X. Zhu, D. Anguelov, and D. Ramanan · 2014
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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A deep learning approach to universal skin disease classification
H. Liao · 2016
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2016
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Choice, transparency, coordination, and quality among direct-to-consumer telemedicine websites and apps treating skin disease
J. Resneck, M. Abrouk, M. Steuer, and et al · 2016
Low-shot visual recognition by shrinking and hallucinating features
B. Hariharan and R. B. Girshick · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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icarl: Incremental classifier and representation learning
S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert · 2017
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Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
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The devil is in the tails: Fine-grained classification in the wild
G. Van Horn and P. Perona · 2017
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Learning to model the tail
Y.-X. Wang, D. Ramanan, and M. Hebert · 2017
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Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra, et al · 2016
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Dermatologist-level classification of skin cancer with deep neural networks
A. Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, and S. Thrun · 2017
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Gaussian prototypical networks for few-shot learning on omniglot
S. Fort · 2017
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A systematic study of the class imbalance problem in convolutional neural networks
M. Buda, A. Maki, and M. A. Mazurowski · 2018
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Thoracic disease identification and localization with limited supervision
Z. Li, C. Wang, M. Han, Y. Xue, W. Wei, L. Li, and F. Li · 2018
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Meta-learning for semi-supervised few-shot classification
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