Tagged: hierarchical pyramid neural network

Application of Information Theory to Improve Computer-Aided Diagnosis

Mammographic Computer-Aided Diagnosis (CAD) systems are an approach for low-cost double reading. Though results to date have been promising, current systems often suffer from unacceptably high false positive rates. Improved methods are needed for optimally setting the system parameters, particularly in the case of statistical models that are common elements of most CAD systems. In this research project we developed a framework for building hierarchical pattern recognizers for CAD based on information theoretic criteria, e.g., the minimum description length (MDL). As part of this framework, we developed a hierarchical image probability (HIP) model. HIP models are well-suited to information theoretic methods since they are generative. We developed architecture search algorithms based on information theory, and applied these to mammographic CAD. The resulting mass detection algorithm, for example, reduced the false positive rate of a CAD system by 30% with no loss of sensitivity. We showed that the criteria reliably correlate with performance on new data. The framework allows many other applications not possible with most pattern recognition algorithms, including rejection of novel examples that can’t be reliably classified, synthesis of artificial images to investigate the structure learned by the model, and compression, which is as good as JPEG.

Learning contextual relationships in mammograms using a hierarchical pyramid neural network

This paper describes a pattern recognition architecture, which we term hierarchical pyramid/neural network (HPNN), that learns to exploit image structure at multiple resolutions for detecting clinically significant features in digital/digitized mammograms. The HPNN architecture consists of a hierarchy of neural networks, each network receiving feature inputs at a given scale as well as features constructed by networks lower in the hierarchy. Networks are trained using a novel error function for the supervised learning of image search/detection tasks when the position of the objects to be found is uncertain or ill defined. We have evaluated the HPNN’s ability to eliminate false positive (FP) regions of interest generated by the University of Chicago’s (UofC) Computer-aided diagnosis (CAD) systems for microcalcification and mass detection. Results show that the HPNN architecture, trained using the uncertain object position (UOP) error function, reduces the FP rate of a mammographic CAD system by approximately 50% without significant loss in sensitivity. Investigation into the types of FPs that the HPNN eliminates suggests that the pattern recognizer is automatically learning and exploiting contextual information. Clinical utility is demonstrated through the evaluation of an integrated system in a clinical reader study. We conclude that the HPNN architecture learns contextual relationships between features at multiple scales and integrates these features for detecting microcalcifications and breast masses.