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

Paul Sajda, C. Spence, L. Parra, RM Nishikawa

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.

Accepted 16 October 2000
See full text

Latest News & Links

See All News