Sebastian Pölsterl
Sebastian Pölsterl
Artificial Intelligence in Medical Imaging, Ludwig Maximilian University, Munich
Потвърден имейл адрес: poelsterl.net - Начална страница
Odefy-from discrete to continuous models
J Krumsiek, S Pölsterl, DM Wittmann, FJ Theis
BMC bioinformatics 11 (1), 1-10, 2010
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open …
J Guinney, T Wang, TD Laajala, KK Winner, JC Bare, EC Neto, SA Khan, ...
The Lancet Oncology 18 (1), 132-142, 2017
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
A Guha Roy, S Siddiqui, S Pölsterl, N Navab, C Wachinger
arXiv e-prints, arXiv: 1905.06731, 2019
scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn.
S Pölsterl
J. Mach. Learn. Res. 21 (212), 1-6, 2020
2d image registration in ct images using radial image descriptors
F Graf, HP Kriegel, M Schubert, S Pölsterl, A Cavallaro
International Conference on Medical Image Computing and Computer-Assisted …, 2011
‘Squeeze & excite’guided few-shot segmentation of volumetric images
AG Roy, S Siddiqui, S Pölsterl, N Navab, C Wachinger
Medical image analysis 59, 101587, 2020
Fast Training of Support Vector Machines for Survival Analysis
S Pölsterl, N Navab, A Katouzian
European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015 …, 2015
Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection
S Pölsterl, S Conjeti, N Navab, A Katouzian
Artificial intelligence in medicine 72, 1-11, 2016
Detect and correct bias in multi-site neuroimaging datasets
C Wachinger, A Rieckmann, S Pölsterl, ...
Medical Image Analysis 67, 101879, 2021
An efficient training algorithm for kernel survival support vector machines
S Pölsterl, N Navab, A Katouzian
arXiv preprint arXiv:1611.07054, 2016
Heterogeneous ensembles for predicting survival of metastatic, castrate-resistant prostate cancer patients
S Pölsterl, P Gupta, L Wang, S Conjeti, A Katouzian, N Navab
F1000Research 5 (2676), 2016
Method to identify optimum coronary artery disease treatment
A Kamen, MK Singh, S Poelsterl, LA Ladic, D Comaniciu
US Patent App. 14/442,517, 2016
A wide and deep neural network for survival analysis from anatomical shape and tabular clinical data
S Pölsterl, I Sarasua, B Gutiérrez-Becker, C Wachinger
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019
Quantifying confounding bias in neuroimaging datasets with causal inference
C Wachinger, BG Becker, A Rieckmann, S Pölsterl
International Conference on Medical Image Computing and Computer-Assisted …, 2019
Position prediction in ct volume scans
F Graf, HP Kriegel, S Pölsterl, M Schubert, A Cavallaro
Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011
Differences in Signaling Patterns on PI3K Inhibition Reveal Context Specificity in KRAS-Mutant CancersContext Specificity of KRAS Signaling: Clinical Implications
A Stewart, EA Coker, S Pölsterl, A Georgiou, AR Minchom, S Carreira, ...
Molecular cancer therapeutics 18 (8), 1396-1404, 2019
Semi-structured deep piecewise exponential models
P Kopper, S Pölsterl, C Wachinger, B Bischl, A Bender, D Rügamer
Survival Prediction-Algorithms, Challenges and Applications, 40-53, 2021
Combining 3d image and tabular data via the dynamic affine feature map transform
S Pölsterl, TN Wolf, C Wachinger
International Conference on Medical Image Computing and Computer-Assisted …, 2021
Adversarial Learned Molecular Graph Inference and Generation
S Pölsterl, C Wachinger
European Conference on Machine Learning and Principles and Practice of …, 2019
Identifying patients with diabetes using discriminative restricted boltzmann machines
P Gupta, U Sivalingam, S Pölsterl, N Navab
Technical report, Technical University of Munich, Germany, 2015
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