Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction-Reference-Cited by-同舟云学术

Deep Learning Versus Classical Regression for Brain Tumor Patient Survival Prediction

Published:2019 Issue: Volume: Page:429-440
ISSN:0302-9743
Container-title:Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
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Author:

Suter Yannick,Jungo Alain,Rebsamen Michael,Knecht Urspeter,Herrmann Evelyn,Wiest Roland,Reyes Mauricio

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-11726-9_38

Reference26 articles.

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2. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. (2017). https://doi.org/10.1038/sdata.2017.117

3. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017). https://doi.org/10.1038/sdata.2017.117

4. Bakas, S., Reyes, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. ArXiv e-prints, November 2018

5. Bakas, S., et al.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017). https://doi.org/10.1038/sdata.2017.117

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