Marking the Path Toward Artificial Intelligence–Based Image Classification in Dermatology-Reference-Cited by-同舟云学术

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Marking the Path Toward Artificial Intelligence–Based Image Classification in Dermatology

Published:2019-10-01 Issue:10 Volume:155 Page:1105
ISSN:2168-6068
Container-title:JAMA Dermatology
language:en
Short-container-title:JAMA Dermatol

Author:

Novoa Roberto A.¹²,Gevaert Olivier³,Ko Justin M.¹

Affiliation:

1. Department of Dermatology, Stanford University School of Medicine, Stanford, California

2. Department of Pathology, Stanford University School of Medicine, Stanford, California

3. Department of Biomedical Data Science, Stanford University, Stanford, California

Publisher

American Medical Association (AMA)

Subject

Dermatology

Link

https://jamanetwork.com/journals/jamadermatology/articlepdf/2740805/jamadermatology_novoa_2019_ed_190017.pdf

Reference12 articles.

1. Dermatologist-level classification of skin cancer with deep neural networks.;Esteva;Nature,2017

2. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.;Haenssle;Ann Oncol,2018

3. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm.;Han;J Invest Dermatol,2018

4. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: automatic construction of onychomycosis datasets by region-based convolutional deep neural network.;Han;PLoS One,2018

5. Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks.;Tschandl;JAMA Dermatol,2019

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