Survival analysis using deep learning with medical imaging
Abstract: There is widespread interest in using deep learning to build prediction models for medical imaging data. These deep learning methods capture the local structure of the image and require no manual feature extraction. Despite the importance of modeling survival in the context of medical data analysis, research on deep learning methods for modeling the relationship of imaging and time-to-event data is still under-developed. We provide an overview of deep learning methods for time-to-event outcomes and compare several deep learning methods to Cox model based methods through the analysis of a histology dataset of gliomas.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Survival analysis using deep learning with medical imaging ; volume:20 ; number:1 ; year:2023 ; pages:1-12 ; extent:12
The international journal of biostatistics ; 20, Heft 1 (2023), 1-12 (gesamt 12)
- Urheber
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Morrison, Samantha
Gatsonis, Constantine
Eloyan, Ani
Steingrimsson, Jon Arni
- DOI
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10.1515/ijb-2022-0113
- URN
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urn:nbn:de:101:1-2405311733071.535117417156
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:48 MESZ
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Beteiligte
- Morrison, Samantha
- Gatsonis, Constantine
- Eloyan, Ani
- Steingrimsson, Jon Arni
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