Publications

    • S. Pölsterl
    • A. Wilkins
    • S. Gulliford
    • et al.
    Big-RT: Big Data Analysis to Identify Combinatorial Predictors of Radiotherapy Toxicity for Personalised Treatment in Prostate Cancer Patients. In: National Cancer Research Institute (NCRI) Cancer Conference. Nov. 2017. Poster presentation. [Poster]
    • A. Stewart
    • E. A. Coker
    • A. Minchom
    • et al.
    A translational phosphoproteomic approach to study differences in KRAS signaling in pancreatic, colorectal and lung cancers. In: Cancer Research. Vol. 77. 13 Supplement. July 2017, pp. 996–996. doi: 10.1158/1538-7445.AM2017-996. AACR Annual Meeting 2017.
    • J. Guinney
    • T. Wang
    • T. D. Laajala
    • et al.
    Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data. In: Lancet Oncology 18.1 (Nov. 2016), pp. 132–142. doi: 10.1016/S1470-2045(16)30560-5.
    • S. Pölsterl
    • G. Pankaj
    • L. Wang
    • et al.
    Heterogeneous Ensembles for Predicting Survival of Metastatic, Castrate-Resistant Prostate Cancer Patients. In: F1000Research 5.2676 (Nov. 2016). doi: 10.12688/f1000research.8231.1.
    • R. Bekmukhametov
    • S. Pölsterl
    • T. Allmendinger
    • M.-D. Doan
    • N. Navab
    Automatic Detection of Non-Biological Artifacts in ECGs Acquired During Cardiac Computed Tomography. In: Machine Learning and Knowledge Discovery in Databases. Sept. 2016, pp. 193–208. doi: 10.1007/978-3-319-46131-1_24. Acceptance rate: 10 of 50 papers (20%). [Poster] [Presentation]
    • S. Pölsterl
    • N. Navab
    • A. Katouzian
    An Efficient Training Algorithm for Kernel Survival Support Vector Machines. In: 3rd Workshop on Machine Learning in Life Sciences. Sept. 2016. arXiv: 1611.07054 [cs.LG]. [Poster] [Presentation]
    • S. Pölsterl
    • S. Conjeti
    • N. Navab
    • A. Katouzian
    Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection. In: Artificial Intelligence in Medicine 72 (July 2016), pp. 1–11. doi: 10.1016/j.artmed.2016.07.004.
    • S. Pölsterl
    Algorithms for Large-scale Learning from Heterogeneous Survival Data. Dissertation (May 2016). [PDF]
    • S. Pölsterl
    • N. Navab
    • A. Katouzian
    Fast Training of Support Vector Machines for Survival Analysis. In: Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science. 2015, pp. 243–259. doi: 10.1007/978-3-319-23525-7_15. Acceptance rate: 89 of 383 papers (23.2%). [Poster] [Presentation]
    • S. Pölsterl
    • M. Singh
    • A. Katouzian
    • et al.
    Stratification of coronary artery disease patients for revascularization procedure based on estimating adverse effects. In: BMC Medical Informatics and Decision Making 15.1 (2015), p. 9. doi: 10.1186/s12911-015-0131-0.
    • F. Graf
    • H.-P. Kriegel
    • S. Pölsterl
    • M. Schubert
    • A. Cavallaro
    Position prediction in CT volume scans. In: Proc. of the 28 th International Conference on Machine Learning (ICML). Workshop on Learning for Global Challenges. 2011.
    • F. Graf
    • H.-P. Kriegel
    • M. Schubert
    • S. Pölsterl
    • A. Cavallaro
    2D Image Registration in CT Images Using Radial Image Descriptors. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI) (2011), pp. 607–614. doi: 10.1007/978-3-642-23629-7_74. Acceptance rate: 251 of 819 papers (30.6%).
    • J. Krumsiek
    • S. Pölsterl
    • D. M. Wittmann
    • F. J. Theis
    • Odefy – From discrete to continuous models. In: BMC Bioinformatics 11.1 (2010), p. 233. doi: 10.1186/1471-2105-11-233.