Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016GL072033 |
Dermatologist-level classification of skin cancer with deep neural networks | |
Esteva, Andre1; Kuprel, Brett1; Novoa, Roberto A.2,3; Ko, Justin2; Swetter, Susan M.2,4; Blau, Helen M.5; Thrun, Sebastian6 | |
2017-02-02 | |
发表期刊 | NATURE
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ISSN | 0028-0836 |
EISSN | 1476-4687 |
出版年 | 2017 |
卷号 | 542期号:7639页码:115-+ |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Skin cancer, the most common human malignancy(1-3), is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs)(4,5) show potential for general and highly variable tasks across many fine-grained object categories(6-11). Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images-two orders of magnitude larger than previous datasets(12)-consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care. |
领域 | 地球科学 ; 气候变化 ; 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000396119300042 |
WOS关键词 | MELANOMA ; DIAGNOSIS ; ACCURACY ; LESIONS |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/29037 |
专题 | 气候变化 |
作者单位 | 1.Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA; 2.Stanford Univ, Dept Dermatol, Stanford, CA 94305 USA; 3.Stanford Univ, Dept Pathol, Stanford, CA 94305 USA; 4.Vet Affairs Palo Alto Hlth Care Syst, Dept Serv, Palo Alto, CA USA; 5.Stanford Univ, Inst Stem Cell Biol & Regenerat Med, Dept Microbiol & Immunol, Baxter Lab Stem Cell Biol, Stanford, CA 94305 USA; 6.Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA |
推荐引用方式 GB/T 7714 | Esteva, Andre,Kuprel, Brett,Novoa, Roberto A.,et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. NATURE,2017,542(7639):115-+. |
APA | Esteva, Andre.,Kuprel, Brett.,Novoa, Roberto A..,Ko, Justin.,Swetter, Susan M..,...&Thrun, Sebastian.(2017).Dermatologist-level classification of skin cancer with deep neural networks.NATURE,542(7639),115-+. |
MLA | Esteva, Andre,et al."Dermatologist-level classification of skin cancer with deep neural networks".NATURE 542.7639(2017):115-+. |
条目包含的文件 | 条目无相关文件。 |
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