Survival analysis using deep learning with medical imaging

Morrison Samantha, Gatsonis Constantine, Eloyan Ani and Steingrimsson Jon Arni ()
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Morrison Samantha: Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA
Gatsonis Constantine: Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA
Eloyan Ani: Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA
Steingrimsson Jon Arni: Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA

The International Journal of Biostatistics, 2024, vol. 20, issue 1, 1-12

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.

Keywords: convolutional neural networks; Doubly Robust estimation; survival analysis; time-to-event outcomes (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/ijb-2022-0113

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