Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks-Reference-Cited by-同舟云学术

Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel Networks

Published:2021 Issue: Volume: Page:228-240
ISSN:0302-9743
Container-title:Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
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Author:

Patel Jay,Chang Ken,Hoebel Katharina,Gidwani Mishka,Arun Nishanth,Gupta Sharut,Aggarwal Mehak,Singh Praveer,Rosen Bruce R.,Gerstner Elizabeth R.,Kalpathy-Cramer Jayashree

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-72087-2_20

Reference32 articles.

1. Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2016), pp. 265–283 (2016). https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf

2. 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

3. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection [Data Set]. Cancer Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF

4. Bakas, S., et al.: Segmentation labels for the pre-operative scans of the TCGA-GBM collection [Data set]. Cancer Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q

5. Bakas, S., et al.: Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (2018)

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