... International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. International Workshop on Computational Advances in Multi-Sensor Adaptive Processing最新文献
{"title":"EXTRACTING INTERPRETABLE FEATURES FOR FETAL HEART RATE RECORDINGS WITH GAUSSIAN PROCESSES.","authors":"Guanchao Feng, J Gerald Quirk, Petar M Djurić","doi":"10.1109/CAMSAP45676.2019.9022670","DOIUrl":"https://doi.org/10.1109/CAMSAP45676.2019.9022670","url":null,"abstract":"<p><p>During labor, fetal heart rate (FHR) and uterine activity (UA) are continuously monitored with Cardiotocography (CTG). The FHR and UA signals are visually inspected by obstetricians to assess the fetal well-being. However, due to the subjectivity of the visual inspection, the evaluations of CTG recordings performed by obstetricians have high inter- and intra-variability. The computerized analysis of FHR relies on features either hand-crafted by experts or automatically learned by machine learning methods. However, the popular interpretable FHR features, in general, have low correlation with the pH value of the umbilical cord blood at birth, which is the current gold standard for labeling FHRs in the computerized analysis of FHRs. The features found by machine learning methods, by contrast, usually have limited interpretability. In this paper, in a follow up of our previous work on FHR analysis using Gaussian processes (GPs), we explore the possibility of using the hyperparameters of GPs as interpretable features. Our results indicate that some GP features achieve high correlation with the pH values, while at the same time they are not highly correlated with other popular features.</p>","PeriodicalId":87214,"journal":{"name":"... International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"2019 ","pages":"381-385"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CAMSAP45676.2019.9022670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25343305","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}
Burhaneddin Yaman, Sebastian Weingärtner, Nikolaos Kargas, Nicholas D Sidiropoulos, Mehmet Akçakaya
{"title":"Locally Low-Rank Tensor Regularization for High-Resolution Quantitative Dynamic MRI.","authors":"Burhaneddin Yaman, Sebastian Weingärtner, Nikolaos Kargas, Nicholas D Sidiropoulos, Mehmet Akçakaya","doi":"10.1109/CAMSAP.2017.8313075","DOIUrl":"10.1109/CAMSAP.2017.8313075","url":null,"abstract":"<p><p>Quantitative dynamic MRI acquisitions have the potential to diagnose diffuse diseases in conjunction with functional abnormalities. However, their resolutions are limited due to the long acquisition time. Such datasets are multi-dimensional, exhibiting interactions between ≥ 4 dimensions, which cannot be easily identified using sparsity or low-rank matrix methods. Hence, low-rank tensors are a natural fit to model such data. But in the presence of multitude of different tissue types in the field-of-view, it is difficult to find an appropriate value of tensor rank, which avoids under- or over-regularization. In this work, we propose a locally low-rank tensor regularization approach to enable high-resolution quantitative dynamic MRI. We show this approach successfully enables dynamic <i>T</i> <sub>1</sub> mapping at high spatio-temporal resolutions.</p>","PeriodicalId":87214,"journal":{"name":"... International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. International Workshop on Computational Advances in Multi-Sensor Adaptive Processing","volume":"2017 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CAMSAP.2017.8313075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37505424","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}