Last Posted: Feb 13, 2020
- A Leukocyte Infiltration Score Defined by a Gene Signature Predicts Melanoma Patient Prognosis.
Zhao Yanding et al. Molecular cancer research : MCR 2019 17(1) 109-119 - Multiphoton microscopy of the dermoepidermal junction and automated identification of dysplastic tissues with deep learning.
Huttunen Mikko J et al. Biomedical optics express 2020 Jan 11(1) 186-199 - Testing for BRAF fusions in patients with advanced BRAF / NRAS / KIT wild-type melanomas permits to identify patients who could benefit of anti-MEK targeted therapy.
Le Flahec Glen et al. Journal of clinical pathology 2020 Feb 73(2) 116-119 - Toward a precision behavioral medicine approach to addressing high-risk sun exposure: a qualitative analysis.
Stump Tammy K et al. JAMIA open 2019 Dec 2(4) 547-553 - Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies
K Freeman, et al, BMJ. February 2020 - Artificial intelligence and melanoma detection: friend or foe of dermatologists?
Charalambides Maria et al. British journal of hospital medicine (London, England : 2005) 2020 Jan 81(1) 1-5 - Characterizing the Role of Dermatologists in Developing AI for Assessment of Skin Cancer: A Systematic Review.
Zakhem George A et al. Journal of the American Academy of Dermatology 2020 Jan - Skin cancer diagnosis based on optimized convolutional neural network.
Zhang Ni et al. Artificial intelligence in medicine 2020 Jan 102101756 - What is AI? Applications of artificial intelligence to dermatology.
Du-Harpur X et al. The British journal of dermatology 2020 Jan - Parent and child perspectives on family interactions related to melanoma risk and prevention after CDKN2A/p16 testing of minor children.
Wu Yelena P et al. Journal of community genetics 2020 Jan
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