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  Indian J Med Microbiol
 

Figure 4: Mouse prostatic intraepithelial neoplasia lesion classifier model based on features obtained by machine learning. (a) Lesions sorted according to positive class conditional probabilities obtained during the 1000 repetitions of the classifier design by random hold-out of a training sample. Training samples are marked with colors (red, advanced phenotype; green, mild phenotype). A probability threshold of 0.5 is marked with a dashed line. (b) Examples of lesion region of interests representing the two phenotypes in the classification model. Examples of both training samples and classified samples are shown. It must be noted that the lesions are not in the same scale. (c) The histogram bins showing the number of times the features have been selected in the classifier model

Figure 4: Mouse prostatic intraepithelial neoplasia lesion classifier model based on features obtained by machine learning. (a) Lesions sorted according to positive class conditional probabilities obtained during the 1000 repetitions of the classifier design by random hold-out of a training sample. Training samples are marked with colors (red, advanced phenotype; green, mild phenotype). A probability threshold of 0.5 is marked with a dashed line. (b) Examples of lesion region of interests representing the two phenotypes in the classification model. Examples of both training samples and classified samples are shown. It must be noted that the lesions are not in the same scale. (c) The histogram bins showing the number of times the features have been selected in the classifier model