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Abstract:
We have previously presented a coarse-to-fine hierarchical
pyramid/neural network (HPNN) architecture which combines
multi-scale image processing techniques with neural networks. In
this paper we present applications of this general architecture to
two problems in mammographic Computer-Aided Diagnosis (CAD). The
first application is the detection of microcalcifications. The
coarse-to-fine HPNN was designed to learn large-scale context
information for detecting small objects like microcalcifications.
Receiver operating characteristic (ROC) analysis suggests that the
hierarchical architecture improves detection performance of a well
established CAD system by roughly 50. The second application is to
detect mammographic masses directly. Since masses are large,
extended objects, the coarse-to-fine HPNN architecture is not
suitable for this problem. Instead we construct a fine-to-coarse
HPNN architecture which is designed to learn small-scale detail
structure associated with the extended objects. Our initial results
applying the fine-to-coarse HPNN to mass detection are encouraging,
with detection performance improvements of 15 to 30. We conclude
that the ability of the HPNN architecture to integrate information
across scales, both coarse-to-fine and fine-to-coarse, makes it
well suited for detecting objects which may have contextual clues
or detail structure occurring at scales other than the natural
scale of the object.
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