Diagnosing pulmonary embolism by integrating patient-level diagnosis and embolus-level detection

A computer program product, comprising:

• a non-transitory computer readable medium comprising code to perform the steps of:
• receiving candidates for classification as true positives and false positives, wherein the candidates are associated with one of a plurality of groups;
• determining a classification boundary for the true positives and the false positives;
• calculating hinge errors for the received candidates based, at least in part, on the determined classification boundary;
• setting a portion of the calculated hinge errors to zero, wherein the portion comprises hinge errors corresponding to candidates on a false positive side of the determined classification boundary belonging to a group of the plurality of groups also having candidates on a true positive side of the determined classification boundary;
• determining a shifted classification boundary based, at least in part, on the hinge errors after setting the portion of the calculated hinge errors to zero;
• and
• identifying as true positives the candidates on a true positive side of the shifted classification boundary.

An apparatus, comprising:

• a memory;
• and
• a processor coupled to the memory, wherein the processor is configured to perform the steps of:
• receiving candidates for classification as true positives and false positives, wherein the candidates are associated with one of a plurality of groups;
• determining a classification boundary for the true positives and the false positives;
• calculating hinge errors for the received candidates based, at least in part, on the determined classification boundary;
• setting a portion of the calculated hinge errors to zero, wherein the portion comprises hinge errors corresponding to candidates on a false positive side of the determined classification boundary belonging to a group of the plurality of groups also having candidates on a true positive side of the determined classification boundary;
• determining a shifted classification boundary based, at least in part, on the hinge errors after setting the portion of the calculated hinge errors to zero;
• and
• identifying as true positives the candidates on a true positive side of the shifted classification boundary.