Linked Life Data

21550892

Source:http://linkedlifedata.com/resource/pubmed/id/21550892

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2011-7-19
pubmed:abstractText
We present a general model using supervised learning and MAP estimation that is capable of performing many common tasks in automated skin lesion diagnosis. We apply our model to segment skin lesions, detect occluding hair, and identify the dermoscopic structure pigment network. Quantitative results are presented for segmentation and hair detection and are competitive when compared to other specialized methods. Additionally, we leverage the probabilistic nature of the model to produce receiver operating characteristic curves, show compelling visualizations of pigment networks, and provide confidence intervals on segmentations.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jul
pubmed:issn
1558-0032
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
15
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
622-9
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Generalizing common tasks in automated skin lesion diagnosis.
pubmed:affiliation
Department of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada. pwighton@sfu.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't