Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach
Sign Up to like & getrecommendations! Published in 2020 at "Medical image analysis"
DOI: 10.1016/j.media.2020.101636
Abstract: Cardiac magnetic resonance imaging (MRI) provides a wealth of imaging biomarkers for cardiovascular disease care and segmentation of cardiac structures is required as a first step in enumerating these biomarkers. Deep convolutional neural networks (CNNs)… read more here.
Keywords: segmentation; convolutional neural; training datasets; approach ... See more keywords
Contrastive representation learning of inorganic materials to overcome lack of training datasets.
Sign Up to like & getrecommendations! Published in 2022 at "Chemical communications"
DOI: 10.1039/d2cc01764d
Abstract: Data representation forms a feature space where forms data distribution that is one of the key factors determining the prediction accuracy of machine learning (ML). In particular, the data representation is crucial to handle small… read more here.
Keywords: training datasets; contrastive representation; learning inorganic; representation learning ... See more keywords
A Study of Periodograms Standardized Using Training Datasets and Application to Exoplanet Detection
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2017.2652391
Abstract: When the noise affecting time series is colored with unknown statistics, a difficulty for sinusoid detection is to control the true significance level of the test outcome. This paper investigates the possibility of using training… read more here.
Keywords: periodograms standardized; detection; standardized using; exoplanet detection ... See more keywords