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Abstract: In this paper we summarize our results for two classes of hierarchical multi-scale models that exploit contextual information for detection of structure in mammographic imagery. The first model, the hierarchical pyramid neural network (HPNN), is a discriminative model which is capable of integrating information either coarse-to-fine or fine-tocoarse for microcalcification and mass detection. The second model, the hierarchical image probability (HIP) model, captures short-range and contextual... (Update)
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BibTeX entry: (Update)
@misc{ sajda-capturing,
author = "Paul Sajda and Clay Spence and Lucas Parra",
title = "Capturing Contextual Dependencies In Medical Imagery Using Hierarchical
Multi-Scale Models",
url = "citeseer.ist.psu.edu/sajda02capturing.html" }
Citations (may not include all citations):184 The design and use of steerable filters - Freeman, Adelson - 1991
53 Smart sensing within a pyramid vision machine (context) - Burt - 1988
4 Computerized detection of clustered microcalcifications in d.. (context) - Zhang - 1994
3 Detection, synthesis and compression in mammographic image a.. - Spence, Parra et al. - 2001
2 Digital radiography: A useful clinical tool for computer-aid.. (context) - Doi - 1993
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