Search Results for author: Maarten van Smeden

Found 6 papers, 3 papers with code

Common Limitations of Image Processing Metrics: A Picture Story

1 code implementation12 Apr 2021 Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, M. Jorge Cardoso, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Keyvan Farahani, Bram van Ginneken, Ben Glocker, Patrick Godau, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Alexandros Karargyris, Alan Karthikesalingam, Bernhard Kainz, Emre Kavur, Hannes Kenngott, Jens Kleesiek, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G. M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clarisa Sanchez Gutierrez, Julien Schroeter, Anindo Saha, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Sotirios A. Tsaftaris, Ben van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Annette Kopp-Schneider, Paul Jäger, Lena Maier-Hein

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.

Instance Segmentation object-detection +2

mecor: An R package for measurement error correction in linear regression models with a continuous outcome

no code implementations9 Feb 2021 Linda Nab, Maarten van Smeden, Ruth H. Keogh, Rolf H. H. Groenwold

In the package mecor, regression calibration methods and a maximum likelihood method are implemented to correct for measurement error in a continuous covariate in regression analyses.

Methodology 62-04

Comparing methods addressing multi-collinearity when developing prediction models

1 code implementation5 Jan 2021 Artuur M. Leeuwenberg, Maarten van Smeden, Johannes A. Langendijk, Arjen van der Schaaf, Murielle E. Mauer, Karel G. M. Moons, Johannes B. Reitsma, Ewoud Schuit

When predictors in such models are highly collinear, unexpected or spurious predictor-outcome associations may occur, thereby potentially reducing face-validity and explainability of the prediction model.

Dimensionality Reduction Methodology 60 G.3

Propensity score estimation using classification and regression trees in the presence of missing covariate data

no code implementations25 Jul 2018 Bas B. L. Penning de Vries, Maarten van Smeden, Rolf H. H. Groenwold

Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation.

General Classification Imputation

Impact of predictor measurement heterogeneity across settings on performance of prediction models: a measurement error perspective

1 code implementation27 Jun 2018 Kim Luijken, Rolf H. H. Groenwold, Ben van Calster, Ewout W. Steyerberg, Maarten van Smeden

Although such heterogeneity in predictor measurement across derivation and validation samples is very common, the impact on the performance of prediction models at external validation is not well-studied.

Methodology 97K80

Cannot find the paper you are looking for? You can Submit a new open access paper.