Sebastian Pölsterl
Sebastian Pölsterl
Computational Pathology, Oncology R&D, AstraZeneca
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Cited by
Cited by
scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn
S Pölsterl
Journal of Machine Learning Research 21 (212), 1-6, 2020
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
‘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
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
Odefy-from discrete to continuous models
J Krumsiek, S Pölsterl, DM Wittmann, FJ Theis
BMC bioinformatics 11, 1-10, 2010
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
2d image registration in ct images using radial image descriptors
F Graf, HP Kriegel, M Schubert, S Pölsterl, A Cavallaro
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011: 14th …, 2011
Detect and correct bias in multi-site neuroimaging datasets
C Wachinger, A Rieckmann, S Pölsterl, ...
Medical Image Analysis 67, 101879, 2021
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
An efficient training algorithm for kernel survival support vector machines
S Pölsterl, N Navab, A Katouzian
arXiv preprint arXiv:1611.07054, 2016
Method to identify optimum coronary artery disease treatment
A Kamen, MK Singh, S Poelsterl, LA Ladic, D Comaniciu
US Patent 11,450,431, 2022
Combining 3D image and tabular data via the dynamic affine feature map transform
S Pölsterl, TN Wolf, C Wachinger
Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021
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
Machine Learning and Knowledge Discovery in Databases: International …, 2020
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
Vox2cortex: Fast explicit reconstruction of cortical surfaces from 3d mri scans with geometric deep neural networks
F Bongratz, AM Rickmann, S Pölsterl, C Wachinger
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
Quantifying confounding bias in neuroimaging datasets with causal inference
C Wachinger, BG Becker, A Rieckmann, S Pölsterl
Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019
Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv 2019
AG Roy, S Siddiqui, S Pölsterl, N Navab, C Wachinger
arXiv preprint arXiv:1905.06731, 0
Daft: A universal module to interweave tabular data and 3d images in cnns
TN Wolf, S Pölsterl, C Wachinger, ...
NeuroImage 260, 119505, 2022
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
Is a PET all you need? A multi-modal study for Alzheimer’s disease using 3D CNNs
M Narazani, I Sarasua, S Pölsterl, A Lizarraga, I Yakushev, C Wachinger
International Conference on Medical Image Computing and Computer-Assisted …, 2022
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