Active Relearning for Robust Supervised Training of Emphysema Patterns.
Radiologists are adept at recognizing the character and extent of lung parenchymal abnormalities in computed tomography (CT) scans. However, the inconsistent differential diagnosis due to subjective aggregation necessitates the exploration of automated classification based on supervised or unsupervi...
| Publicado en: | Journal of Digital Imaging Vol. 27; no. 4; pp. 548 - 556 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2014
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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=96967534&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 96967534 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2014 vid: 27 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 96967534 96967534 96967534 10.1007/s10278-014-9686-z 96967534 ppf: 548 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Active Relearning for Robust Supervised Training of Emphysema Patterns. aug: au: Raghunath, Sushravya Rajagopalan, Srinivasan Karwoski, Ronald Bartholmai, Brian Robb, Richard affil: Department of Physiology and Biomedical Engineering, Mayo Clinic, 200 First Street SW Rochester 55905 USA sug: subj: Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Emphysema Classification Feedback Severity of Illness ab: Radiologists are adept at recognizing the character and extent of lung parenchymal abnormalities in computed tomography (CT) scans. However, the inconsistent differential diagnosis due to subjective aggregation necessitates the exploration of automated classification based on supervised or unsupervised learning. The robustness of supervised learning depends on the training samples. Towards optimizing emphysema classification, we introduce a physician- in- the- loop feedback approach to minimize ambiguity in the selected training samples. An experienced thoracic radiologist selected 412 regions of interest (ROIs) across 15 datasets to represent 124, 129, 139 and 20 training samples of mild, moderate, severe emphysema and normal appearance, respectively. Using multi-view (multiple metrics to capture complementary features) inductive learning, an ensemble of seven un-optimized support vector models (SVM) each based on a specific metric was constructed in less than 6 s. The training samples were classified using seven SVM models and consensus labels were created using majority voting. In the active relearning phase, the ensemble- expert label conflicts were resolved by the expert. The efficacy and generality of active relearning feedback was assessed in the optimized parameter space of six general purpose classifiers across the seven dissimilarity metrics. The proposed just- in- time active relearning feedback with un-optimized SVMs yielded 15 % increase in classification accuracy and 25 % reduction in the number of support vectors. The average improvement in accuracy of six classifiers in their optimized parameter space was 21 %. The proposed cooperative feedback method enhances the quality of training samples used to construct automated classification of emphysematous CT scans. Such an approach could lead to substantial improvement in quantification of emphysema. pubtype: Academic Journal doctype: diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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