Source:http://linkedlifedata.com/resource/pubmed/id/17270917
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
2007-2-2
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| pubmed:abstractText |
We investigated Bayesian network structure learning and probability estimation from mammographic feature data in order to classify breast lesions into different pathological categories. We compared the learned networks to naive Bayes classifiers, which are similar to the expert systems previously investigated for breast lesion classification. The learned network structures reflect the difference in the classification of biopsy outcome and the invasiveness of malignant lesions for breast masses and microcalcifications. The difference between masses and microcalcifications should be taken into consideration when interpreting systems for automatic pathological classification of breast lesions. The difference may also affect use of these systems for tasks such as estimating the sampling error of biopsy.
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| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:status |
PubMed-not-MEDLINE
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| pubmed:issn |
1557-170X
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
4
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| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
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| pubmed:pagination |
3031-4
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| pubmed:year |
2004
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| pubmed:articleTitle |
Bayesian networks of BI-RADStrade mark descriptors for breast lesion classification.
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| pubmed:affiliation |
Dept. of Biomed. Eng., Texas Univ., Austin, TX, USA.
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| pubmed:publicationType |
Journal Article
|