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      Dermatologist-level classification of skin cancer with deep neural networks

      , , , , , ,
      Nature
      Springer Nature

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          Abstract

          Skin cancer, the most common human malignancy, 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) show potential for general and highly variable tasks across many fine-grained object categories. 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—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.

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          Most cited references5

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          Model predicting survival in stage I melanoma based on tumor progression.

          We used the lesional steps in tumor progression and multivariable logistic regression to develop a prognostic model for primary, clinical stage I cutaneous melanoma. This model is 89% accurate in predicting survival. Using histologic criteria, we assigned melanomas to tumor progression steps by ascertaining their particular growth phase. These phases were the in situ and invasive radial growth phase and the vertical growth phase (the focal formation of a dermal tumor nodule or dermal tumor plaque within the radial growth phase or such dermal growth without an evident radial growth phase). After a minimum follow-up of 100.6 months and a median follow-up of 150.2 months, 122 invasive radial-growth-phase tumors were found to be without metastases. Eight-year survival among the 264 patients whose tumors had entered the vertical growth phase was 71.2%. Survival prediction in these patients was enhanced by the use of a multivariable logistic regression model. Twenty-three attributes were tested for entry into this model. Six had independently predictive prognostic information: (a) mitotic rate per square millimeter, (b) tumor-infiltrating lymphocytes, (c) tumor thickness, (d) anatomic site of primary melanoma, (e) sex of the patient, and (f) histologic regression. When mitotic rate per square millimeter, tumor-infiltrating lymphocytes, primary site, sex, and histologic regression are added to a logistic regression model containing tumor thickness alone, they are independent predictors of 8-year survival (P less than .0005).
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            Is Open Access

            Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

            Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.
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              Accuracy of Computer Diagnosis of Melanoma

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                Author and article information

                Journal
                Nature
                Nature
                Springer Nature
                0028-0836
                1476-4687
                January 25 2017
                January 25 2017
                :
                :
                Article
                10.1038/nature21056
                28117445
                0174c7ef-ed8d-4ff3-b732-329415975ff7
                © 2017
                History

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