Image-based survival prediction for lung cancer patients using CNNS
Haarburger C, Weitz P, Rippel O, Merhof D (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: IEEE Computer Society
Book Volume: 2019-April
Pages Range: 1197-1201
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Venice, ITA
ISBN: 9781538636411
DOI: 10.1109/ISBI.2019.8759499
Abstract
Traditional survival models such as the Cox proportional hazards model are typically based on scalar or categorical clinical features. With the advent of increasingly large image datasets, it has become feasible to incorporate quantitative image features into survival prediction. So far, this kind of analysis is mostly based on radiomics features, i.e. a fixed set of features that is mathematically defined a priori. To capture highly abstract information, it is desirable to learn the feature extraction using convolutional neural networks. However, for tomographic medical images, model training is difficult because on the one hand, only few samples of 3D image data fit into one batch at once and on the other hand, survival loss functions are essentially ordering measures that require large batch sizes. In this work, we show that by simplifying survival analysis to median survival classification, convolutional neural networks can be trained with small batch sizes and learn features that predict survival equally well as end-to-end hazard prediction networks. Our approach outperforms (mean c-index =0.623) the previous state of the art (mean c-index =0.609) on a publicly available lung cancer dataset.
Involved external institutions
Germany (DE)How to cite
APA:
Haarburger, C., Weitz, P., Rippel, O., & Merhof, D. (2019). Image-based survival prediction for lung cancer patients using CNNS. In Proceedings - International Symposium on Biomedical Imaging (pp. 1197-1201). Venice, ITA: IEEE Computer Society.
MLA:
Haarburger, Christoph, et al. "Image-based survival prediction for lung cancer patients using CNNS." Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, ITA IEEE Computer Society, 2019. 1197-1201.
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