Abstract: A method for generating object classification models is disclosed. Initially, a set of training data is fed into a training algorithm to generate a first object classification model. A set of field data is then applied to the first object classification model to produce a set of field object classifications. The number of data in the set of field data is significantly less than the number of data in the set of training data. Finally, the set of field object classifications and the set of field data are fed into the training algorithm to generate a second object classification model. The second object classification model can be utilized for predicting object classifications.

Abstract: A method, computer program product, and data processing system for building a training set and classifier model for tissue classification from MRI images using limited training data are disclosed. In a preferred embodiment, the method begins with a given set of multispectral MRI scans of an abdominal slice of a human organ. A clustering algorithm is applied to the image data to cluster different objects in the image into unique clusters. A deterministic initialization procedure is applied to the clustering algorithm to ensure solution uniqueness, convergence, and the creation of meaningful clusters. A human domain expert then produces a corrected set of clusters by retaining only clusters of interest. A training set is generated that represents samples of each of the tissue types of interest, as well as a validation set. One or more classifiers are constructed from the training set and then evaluated for accuracy using the validation set.

Abstract: A method for generating object classification models is disclosed. Initially, a set of training data is fed into a training algorithm to generate a first object classification model. A set of field data is then applied to the first object classification model to produce a set of field object classifications. The number of data in the set of field data is significantly less than the number of data in the set of training data. Finally, the set of field object classifications and the set of field data are fed into the training algorithm to generate a second object classification model. The second object classification model can be utilized for predicting object classifications.