A two layer case-based reasoning architecture for medical image understanding

The paper describes a novel architecture for image understanding. It is based on acquisition of radiologist knowledge, and combines low-level structure analysis with high-level interpretation of image content, within a task-oriented model. A case based reasoner working on a segment case-base contains the individual image segments. These cases with labels are considered indexes for another case based reasoner working on an organ interpretation case base. Both are Creek type case based reasoners, here operating within a propose-critique-modify task structure. Methods for criticizing suggested interpretations by way of explanation, and how interpretations may be modified, are presented. An example run illustrates the system architecture and its key concepts.