Abstract: A computer-implemented method for detecting lung abnormalities is provided. The method includes the steps of: acquiring a chest image of a subject; labeling one or more regions of interest (ROIs) in the chest image; segmenting a fine-grained boundary of each of the labeled ROIs; generating a plurality of output matrixes for each of the segmented ROIs; sorting a plurality of prediction scores obtained in the output matrixes, and generating a plurality of recommendations of potential abnormalities for each of the ROIs; and outputting the recommendations, A smart imagery framing and truthing (SIFT) system for implementing the method is also provided.

Abstract: A system and method for improved detection of objects of interest in image data using adaptive stepwise classification and hierarchical decision diagrams to manage false positives is provided. The present invention uses an adaptive stepwise classification approach, preferably based on a hierarchical binary decision diagram (BDD), to enable the efficient management of false positive objects to improve detection performance. The present invention is particularly suited for the reduction of false positives during the detection of acid fast bacilli associated with tuberculosis.

Abstract: A system and method for improved detection of objects of interest in image data using adaptive stepwise classification and hierarchical decision diagrams to manage false positives is provided. The present invention uses an adaptive stepwise classification approach, preferably based on a hierarchical binary decision diagram (BDD), to enable the efficient management of false positive objects to improve detection performance. The present invention is particularly suited for the reduction of false positives during the detection of acid fast bacilli associated with tuberculosis.