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Author |
D. Seron; F. Moreso; C. Gratin; Jordi Vitria; E. Condom |
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Automated classification of renal interstitium and tubules by local texture analysis and a neural network |
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1996 |
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Analytical and Quantitative Cytology and Histology |
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18 |
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5 |
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410-9, PMID: 8908314 |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ SMG1996 |
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76 |
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Author |
Agata Lapedriza; Santiago Segui; David Masip; Jordi Vitria |
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Title |
A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning |
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2008 |
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Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications, |
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11 |
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3-4 |
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299-308 |
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Springer |
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OR;MV |
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BCNPCL @ bcnpcl @ LSM2008 |
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996 |
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Author |
Maria Vanrell; Jordi Vitria; Xavier Roca |
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A multidimensional scaling approach to explore the behavior of a texture perception algorithm. |
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1997 |
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Machine Vision and Applications |
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9 |
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262–271 |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ VVR1997 |
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35 |
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Bogdan Raducanu; Jordi Vitria |
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Online Nonparametric Discriminant Analysis for Incremental Subspace Learning and Recognition |
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2008 |
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Pattern Analysis and Applications. Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications |
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11 |
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3-4 |
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259–268 |
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OR;MV |
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BCNPCL @ bcnpcl @ RaV2008c |
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997 |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Fosca De Iorio; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Intestinal Motility Assessment With Video Capsule Endoscopy: Automatic Annotation of Phasic Intestinal Contractions |
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Year |
2010 |
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IEEE Transactions on Medical Imaging |
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TMI |
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29 |
Issue |
2 |
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246-259 |
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Abstract |
Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions shown in a video provided by an ingestible capsule with a wireless micro-camera. The manual labeling of all the motility events requires large amount of time for offline screening in search of findings with low prevalence, which turns this procedure currently unpractical. In this paper, we propose a machine learning system to automatically detect the phasic intestinal contractions in video capsule endoscopy, driving a useful but not feasible clinical routine into a feasible clinical procedure. Our proposal is based on a sequential design which involves the analysis of textural, color, and blob features together with SVM classifiers. Our approach tackles the reduction of the imbalance rate of data and allows the inclusion of domain knowledge as new stages in the cascade. We present a detailed analysis, both in a quantitative and a qualitative way, by providing several measures of performance and the assessment study of interobserver variability. Our system performs at 70% of sensitivity for individual detection, whilst obtaining equivalent patterns to those of the experts for density of contractions. |
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IEEE |
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0278-0062 |
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800 |
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MILAB;MV;OR;SIAI |
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no |
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Call Number |
BCNPCL @ bcnpcl @ VSD2010; IAM @ iam @ VSI2010 |
Serial |
1281 |
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| Permanent link to this record |
