Survival analysis using deep learning with medical imaging.

Autor:	Morrison S; Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA., Gatsonis C; Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA., Eloyan A; Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA., Steingrimsson JA; Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA.
Jazyk:	angličtina
Zdroj:	The international journal of biostatistics [Int J Biostat] 2023 Jun 14; Vol. 20 (1), pp. 1-12. Date of Electronic Publication: 2023 Jun 14 (Print Publication: 2024).
DOI:	10.1515/ijb-2022-0113
Abstrakt:	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. (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)
Databáze:	MEDLINE
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