Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm-Reference-Cited by-同舟云学术

Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm

Published:2022 Issue: Volume: Page:253-266
ISSN:0302-9743
Container-title:Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
language:
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Author:

Carré Alexandre^ORCID,Deutsch Eric^ORCID,Robert Charlotte^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-09002-8_23

Reference44 articles.

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3. Bakas, S.: Segmentation Labels for the Pre-operative Scans of the TCGA-LGG collection (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF

4. Bakas, S., et al.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data 4(1), 170117 (2017). https://doi.org/10.1038/sdata.2017.117

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. arXiv:1811.02629 [cs, stat] (2019)

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