1. A method, comprising: providing a training data set associated with a series of training images to a computer-assisted diagnosis (CAD) computing device to train the CAD computing device using machine learning, at least a portion of the training data set comprising image features associated with known classes of a plurality of classes, the plurality of classes associated with predetermined possible clinical actions; determining a cost function of weighted error terms based on results of the providing of the training data set to the CAD computing device; weighting certain parameters of the cost function for certain image features values associated with known examples of clinical significance that are predetermined as being important to diagnose; receiving a selected image by an interface, the selected image comprising an image feature; extracting at least one image feature value from the selected image, and applying the at least one image feature value to the CAD computing device trained using the weighted cost function to identify a class from the plurality of classes.