Consensus Versus Disagreement in Imaging Research: a Case Study Using the LIDC Database.
Traditionally, image studies evaluating the effectiveness of computer-aided diagnosis (CAD) use a single label from a medical expert compared with a single label produced by CAD. The purpose of this research is to present a CAD system based on Belief Decision Tree classification algorithm, capable o...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 3; pp. 423 - 437 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104564874&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104564874 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Jun2012 vid: 25 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104564874 75063743 10.1007/s10278-011-9445-3 NLM22193755 104564874 ppf: 423 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Consensus Versus Disagreement in Imaging Research: a Case Study Using the LIDC Database. aug: au: Zinovev, Dmitriy Duo, Yujie Raicu, Daniela Furst, Jacob Armato, Samuel affil: College of Computing and Digital Media, DePaul University, 243 S. Wabash Ave Chicago 60604 USA sug: subj: Diagnostic Imaging Decision Trees Diagnosis, Computer Assisted Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed Thorax Algorithms Evaluation Research ROC Curve Human ab: Traditionally, image studies evaluating the effectiveness of computer-aided diagnosis (CAD) use a single label from a medical expert compared with a single label produced by CAD. The purpose of this research is to present a CAD system based on Belief Decision Tree classification algorithm, capable of learning from probabilistic input (based on intra-reader variability) and providing probabilistic output. We compared our approach against a traditional decision tree approach with respect to a traditional performance metric (accuracy) and a probabilistic one (area under the distance-threshold curve-AuC). The probabilistic classification technique showed notable performance improvement in comparison with the traditional one with respect to both evaluation metrics. Specifically, when applying cross-validation technique on the training subset of instances, boosts of 28.26% and 30.28% were noted for the probabilistic approach with respect to accuracy and AuC, respectively. Furthermore, on the validation subset of instances, boosts of 20.64% and 23.21% were noted again for the probabilistic approach with respect to the same two metrics. In addition, we compared our CAD system results with diagnostic data available for a small subset of the Lung Image Database Consortium database. We discovered that when our CAD system errs, it generally does so with low confidence. Predictions produced by the system also agree with diagnoses of truly benign nodules more often than radiologists, offering the possibility of reducing the false positives. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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