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      Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

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          Most cited references 58

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          A Coefficient of Agreement for Nominal Scales

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            The meaning and use of the area under a receiver operating characteristic (ROC) curve.

            A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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              Going deeper with convolutions

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                Author and article information

                Journal
                Nature Medicine
                Nat Med
                Springer Nature America, Inc
                1078-8956
                1546-170X
                September 17 2018
                Article
                10.1038/s41591-018-0177-5
                © 2018

                http://www.springer.com/tdm

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