IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing最新文献
E Ataer-Cansizoglu, S You, J Kalpathy-Cramer, K Keck, M F Chiang, D Erdogmus
{"title":"OBSERVER AND FEATURE ANALYSIS ON DIAGNOSIS OF RETINOPATHY OF PREMATURITY.","authors":"E Ataer-Cansizoglu, S You, J Kalpathy-Cramer, K Keck, M F Chiang, D Erdogmus","doi":"10.1109/MLSP.2012.6349809","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349809","url":null,"abstract":"<p><p>Retinopathy of prematurity (ROP) is a disease affecting low-birth weight infants and is a major cause of childhood blindness. However, human diagnoses is often subjective and qualitative. We propose a method to analyze the variability of expert decisions and the relationship between the expert diagnoses and features. The analysis is based on Mutual Information and Kernel Density Estimation on features. The experiments are carried out on a dataset of 34 retinal images diagnosed by 22 experts. The results show that a group of observers decide consistently with each other and there are popular features that have a high correlation with labels.</p>","PeriodicalId":73290,"journal":{"name":"IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/MLSP.2012.6349809","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32488306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"EXTRACTING CUES FROM SPEECH FOR PREDICTING SEVERITY OF PARKINSON'S DISEASE.","authors":"Meysam Asgari, Izhak Shafran","doi":"10.1109/MLSP.2010.5589118","DOIUrl":"https://doi.org/10.1109/MLSP.2010.5589118","url":null,"abstract":"<p><p>Speech pathologists often describe voice quality in hypokinetic dysarthria or Parkinsonism as harsh or breathy, which has been largely attributed to incomplete closure of vocal folds. Exploiting its harmonic nature, we separate voiced portion of the speech to obtain an objective estimate of this quality. The utility of the proposed approach was evaluated on predicting 116 clinical ratings of Parkinson's disease on 82 subjects. Our results show that the information extracted from speech, elicited through 3 tasks, can predict the motor subscore (range 0 to 108) of the clinical measure, the Unified Parkinson's Disease Rating Scale, within a mean absolute error of 5.7 and a standard deviation of about 2.0. While still preliminary, our results are significant and demonstrate that the proposed computational approach has promising real-world applications such as in home-based assessment or in telemonitoring of Parkinson's disease.</p>","PeriodicalId":73290,"journal":{"name":"IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing","volume":"2010 ","pages":"462-467"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/MLSP.2010.5589118","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25427474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}