Tagged: Hip

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.

Hierarchical multi-resolution models for object recognition: Applications to mammographic computer-aided diagnosis

A fundamental problem in image analysis is the integration of information across scale to detect and classify objects. We have developed, within a machine learning framework, two classes of multiresolution models for integrating scale information for object detection and classification-a discriminative model called the hierarchical pyramid neural network and a generative model called a hierarchical image probability model. Using receiver operating characteristic analysis, we show that these models can significantly reduce the false positive rates for a well-established computer-aided diagnosis system.

Hierarchical image probability (HIP) models

We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into conditional distributions of feature vectors at one resolution (pyramid level) conditioned on the image information at lower resolutions. We would like to factor this over positions in the pyramid levels to make it tractable, but such factoring may miss long-range dependencies. To capture long-range dependencies, we introduce hidden class labels at each pixel in the pyramid. The result is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree. The model parameters can be found with maximum likelihood estimation using the EM algorithm. We have obtained encouraging preliminary results on the problems of detecting various objects in SAR images and target recognition in optical aerial images.