Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Mendrik, Adriënne"'
Autor:
Sivak, Elizaveta, Pankowska, Paulina, Mendrik, Adrienne, Emery, Tom, Garcia-Bernardo, Javier, Hocuk, Seyit, Karpinska, Kasia, Maineri, Angelica, Mulder, Joris, Nissim, Malvina, Stulp, Gert
The social sciences have produced an impressive body of research on determinants of fertility outcomes, or whether and when people have children. However, the strength of these determinants and underlying theories are rarely evaluated on their predic
Externí odkaz:
http://arxiv.org/abs/2402.00705
Autor:
Boeschoten, Laura, Mendrik, Adriënne, van der Veen, Emiel, Vloothuis, Jeroen, Hu, Haili, Voorvaart, Roos, Oberski, Daniel
We present PORT, a software platform for local data extraction and analysis of digital trace data. While digital trace data collected by private and public parties hold a huge potential for social-scientific discovery, their most useful parts have be
Externí odkaz:
http://arxiv.org/abs/2110.05154
Publikováno v:
PLoS ONE 15(8): e0237009, 2020, pp. 1-16
In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus achieving a similar classification performance is only possible when t
Externí odkaz:
http://arxiv.org/abs/2002.12105
There has recently been great progress in automatic segmentation of medical images with deep learning algorithms. In most works observer variation is acknowledged to be a problem as it makes training data heterogeneous but so far no attempts have bee
Externí odkaz:
http://arxiv.org/abs/2001.08552
In this paper we aim to refine the concept of grand challenges in medical image analysis, based on statistical principles from quantitative and qualitative experimental research. We identify two types of challenges based on their generalization objec
Externí odkaz:
http://arxiv.org/abs/1911.08531
In the medical image analysis field, organizing challenges with associated workshops at international conferences began in 2007 and has grown to include over 150 challenges. Several of these challenges have had a major impact in the field. However, w
Externí odkaz:
http://arxiv.org/abs/1811.03014
Publikováno v:
16th IEEE International Symposium on Biomedical Imaging (ISBI), Venice, 2019, pp. 364-367
Generalization of voxelwise classifiers is hampered by differences between MRI-scanners, e.g. different acquisition protocols and field strengths. To address this limitation, we propose a Siamese neural network (MRAI-NET) that extracts acquisition-in
Externí odkaz:
http://arxiv.org/abs/1810.07430
Autor:
Boeschoten, Laura, Mendrik, Adriënne, van der Veen, Emiel, Vloothuis, Jeroen, Hu, Haili, Voorvaart, Roos, Oberski, Daniel L.
Publikováno v:
In Patterns 11 March 2022 3(3)
Voxelwise classification approaches are popular and effective methods for tissue quantification in brain magnetic resonance imaging (MRI) scans. However, generalization of these approaches is hampered by large differences between sets of MRI scans su
Externí odkaz:
http://arxiv.org/abs/1709.07944
Autor:
Moeskops, Pim, Viergever, Max A., Mendrik, Adriënne M., de Vries, Linda S., Benders, Manon J. N. L., Išgum, Ivana
Publikováno v:
IEEE Transactions on Medical Imaging, 35(5), 1252-1261 (2016)
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes u
Externí odkaz:
http://arxiv.org/abs/1704.03295