Linked Life Data

8451441

Source:http://linkedlifedata.com/resource/pubmed/id/8451441

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1993-4-14
pubmed:abstractText
The authors investigated the potential utility of artificial neural networks as a decision-making aid to radiologists in the analysis of mammographic data. Three-layer, feed-forward neural networks with a back-propagation algorithm were trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists. A network that used 43 image features performed well in distinguishing between benign and malignant lesions, yielding a value of 0.95 for the area under the receiver operating characteristic curve for textbook cases in a test with the round-robin method. With clinical cases, the performance of a neural network in merging 14 radiologist-extracted features of lesions to distinguish between benign and malignant lesions was found to be higher than the average performance of attending and resident radiologists alone (without the aid of a neural network). The authors conclude that such networks may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
AIM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0033-8419
pubmed:author
pubmed:issnType
Print
pubmed:volume
187
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
81-7
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1993
pubmed:articleTitle
Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
pubmed:affiliation
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't