Sebastian Pölsterl und Survival Analysis - Info zur Person

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A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data. Sep 09, Sebastian Pölsterl, Ignacio Sarasua, ...

A Wide and Deep Neural Network for Survival Analysis arXiv

arxiv.org

by S Pölsterl · · Cited by 19 — Submission history. From: Sebastian Pölsterl [view email] [v1] Mon, 9 Sep :37:47 UTC (116 KB). by S Pölsterl · · Cited by 36 — An Efficient Training Algorithm for Kernel Survival Support Vector Machines. Authors:Sebastian Pölsterl, Nassir Navab, Amin Katouzian · Download ... › cs

Fast Training of Support Vector Machines for Survival Analysis |...

link.springer.com

Survival analysis is a commonly used technique to identify important predictors of adverse events and develop guidelines for patient’s treatment in medical...

A Wide and Deep Neural Network for Survival Analysis from ...mwn.de

wachinger.devweb.mwn.de

von S Pölsterl · Zitiert von: 25 — Sebastian Pölsterl, Ignacio Sarasua, Benjamín Gutiérrez-Becker, and Christian. Wachinger. Artificial Intelligence in Medical Imaging (AI-Med),.

Sebastian Pölsterl

2017.pyconuk.org

Semi-Structured Deep Piecewise Exponential Models. ...Researcher App

www.researcher-app.com

Philipp Kopper, Sebastian Pölsterl, Christian Wachinger, Bernd Bischl, Andreas Bender, David Rügamer. We propose a versatile framework for survival analysis ...

Survival analysis for high-dimensional, heterogeneous ...IBM

research.ibm.com

von S Pölsterl · · Zitiert von: 57 — ... medical data: Exploring feature extraction as an alternative to feature selection for Artificial Intelligence in Medicine by Sebastian Pölsterl et al.

Fast Training of Support Vector Machines for Survival Analysis —...

tum-camp.github.io

Navigation. index · next |; Survival Support Vector Machine 2.0 documentation ». © Copyright , Sebastian Pölsterl. Created using Sphinx

PyVideo.org · Sebastian Pölsterlpyvideo.org › sebastian-polsterl

pyvideo.org

Speaker: Sebastian Pölsterl. Image from Introduction to Survival Analysis with scikit survival. Introduction to Survival Analysis with scikit survival.

Survival analysis for high-dimensional, heterogeneous medical data:...

pubmed.ncbi.nlm.nih.gov

If the number of samples is insufficient, feature extraction methods are unable to reliably identify the underlying manifold, which makes them of limited use...

scikit-survival · PyPI

pypi.org

Survival analysis built on top of scikit-learn

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