A system for identifying one or more tissue types within an image patch according to a hierarchical histological taxonomy, the system comprises: a supervised digital pathology database having a set of training image patches stored thereon; and a processor in communication with the supervised digital pathology database and operable to: access the set of training image patches stored in the supervised digital pathology database; develop a feature extractor to generate a training feature vector identifying one or more training tissue segments in each training image patch in the set of training image patches; develop a feature classifier to assign a tissue type to each training tissue segment identified in the training feature vector and to update a class confidence score for that tissue type, the tissue type being assigned according to the hierarchical histological taxonomy; apply the feature extractor to the image patch to identify one or more tissue segments within the image patch; apply the feature classifier to assign a tissue type to the one or more tissue segments identified by the feature extractor for the image patch and to generate a confidence score for each assigned tissue type within the image patch with reference to the class confidence score, wherein applying the feature classifier comprises mapping one or more prediction models to each tissue segment within the image patch to identify the tissue type relative to the training feature vector and class confidence score for that tissue type; compare each confidence score generated by the feature classifier with a class confidence score generated for a normal tissue of that tissue type to determine whether the image patch is associated with normal tissue; and generate a confidence score vector containing the confidence scores for each tissue type identified for a feature vector by the feature classifier and a health state of each tissue type identified. a supervised digital pathology database having a set of training image patches stored thereon; and a processor in communication with the supervised digital pathology database and operable to: access the set of training image patches stored in the supervised digital pathology database; develop a feature extractor to generate a training feature vector identifying one or more training tissue segments in each training image patch in the set of training image patches; develop a feature classifier to assign a tissue type to each training tissue segment identified in the training feature vector and to update a class confidence score for that tissue type, the tissue type being assigned according to the hierarchical histological taxonomy; apply the feature extractor to the image patch to identify one or more tissue segments within the image patch; apply the feature classifier to assign a tissue type to the one or more tissue segments identified by the feature extractor for the image patch and to generate a confidence score for each assigned tissue type within the image patch with reference to the class confidence score, wherein applying the feature classifier comprises mapping one or more prediction models to each tissue segment within the image patch to identify the tissue type relative to the training feature vector and class confidence score for that tissue type; compare each confidence score generated by the feature classifier with a class confidence score generated for a normal tissue of that tissue type to determine whether the image patch is associated with normal tissue; and generate a confidence score vector containing the confidence scores for each tissue type identified for a feature vector by the feature classifier and a health state of each tissue type identified. access the set of training image patches stored in the supervised digital pathology database; develop a feature extractor to generate a training feature vector identifying one or more training tissue segments in each training image patch in the set of training image patches; develop a feature classifier to assign a tissue type to each training tissue segment identified in the training feature vector and to update a class confidence score for that tissue type, the tissue type being assigned according to the hierarchical histological taxonomy; apply the feature extractor to the image patch to identify one or more tissue segments within the image patch; apply the feature classifier to assign a tissue type to the one or more tissue segments identified by the feature extractor for the image patch and to generate a confidence score for each assigned tissue type within the image patch with reference to the class confidence score, wherein applying the feature classifier comprises mapping one or more prediction models to each tissue segment within the image patch to identify the tissue type relative to the training feature vector and class confidence score for that tissue type; compare each confidence score generated by the feature classifier with a class confidence score generated for a normal tissue of that tissue type to determine whether the image patch is associated with normal tissue; and generate a confidence score vector containing the confidence scores for each tissue type identified for a feature vector by the feature classifier and a health state of each tissue type identified.