Multiresolution neural networks for mammographic mass detection

Clay D. Spence; Paul Sajda

doi:10.1117/12.339829

29 January 1999 Multiresolution neural networks for mammographic mass detection

Clay D. Spence, Paul Sajda

Proceedings Volume 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition; (1999) https://doi.org/10.1117/12.339829
Event: The 27th AIPR Workshop: Advances in Computer-Assisted Recognition, 1998, Washington, DC, United States

Abstract

We have previously presented a hierarchical pyramid/neural network (HPNN) architecture which combines multi-scale image processing techniques with neural networks. This coarse-to- fine HPNN was designed to learn large-scale context information for detecting small objects. We have developed a similar architecture to detect mammographic masses (malignant tumors). Since masses are large, extended objects, the coarse-to-fine HPNN architecture is not suitable for the problem. Instead we constructed a fine-to- coarse HPNN architecture which is designed to learn small- scale detail structure associated with the extended objects. Our initial result applying the fine-to-coarse HPNN to mass detection are encouraging, with detection performance improvements of about 30%. We conclude that the ability of the HPNN architecture to integrate information across scales, from fine to coarse in the case of masses, makes it well suited for detecting objects which may have detail structure occurring at scales other than the natural scale of the object.

Citation Download Citation

Clay D. Spence and Paul Sajda "Multiresolution neural networks for mammographic mass detection", Proc. SPIE 3584, 27th AIPR Workshop: Advances in Computer-Assisted Recognition, (29 January 1999); https://doi.org/10.1117/12.339829

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