DermX: An end-to-end framework for explainable automated dermatological diagnosis
Published 2022 View Full Article
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Title
DermX: An end-to-end framework for explainable automated dermatological diagnosis
Authors
Keywords
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Journal
MEDICAL IMAGE ANALYSIS
Volume 83, Issue -, Pages 102647
Publisher
Elsevier BV
Online
2022-10-08
DOI
10.1016/j.media.2022.102647
References
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Related references
Note: Only part of the references are listed.- Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices
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- Explainable skin lesion diagnosis using taxonomies
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- Human–computer collaboration for skin cancer recognition
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- Attention Residual Learning for Skin Lesion Classification
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- Key challenges for delivering clinical impact with artificial intelligence
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- Systematic review of machine learning for diagnosis and prognosis in dermatology
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- Comparison of Dermatologist Density Between Urban and Rural Counties in the United States
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- The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
- (2018) Philipp Tschandl et al. Scientific Data
- Automated detection of erythema migrans and other confounding skin lesions via deep learning
- (2018) Philippe M. Burlina et al. COMPUTERS IN BIOLOGY AND MEDICINE
- The burden of skin disease in the United States
- (2017) Henry W. Lim et al. JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY
- Dermatologist-level classification of skin cancer with deep neural networks
- (2017) Andre Esteva et al. NATURE
- Global Skin Disease Morbidity and Mortality
- (2017) Chante Karimkhani et al. JAMA Dermatology
- The 2016 International League of Dermatological Societies' revised glossary for the description of cutaneous lesions
- (2016) A. Nast et al. BRITISH JOURNAL OF DERMATOLOGY
- The Global Burden of Skin Disease in 2010: An Analysis of the Prevalence and Impact of Skin Conditions
- (2013) Roderick J. Hay et al. JOURNAL OF INVESTIGATIVE DERMATOLOGY
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