HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation

Author:

Qamar SaqibORCID,Ahmad ParvezORCID,Shen Linlin

Publisher

Springer International Publishing

Reference19 articles.

1. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. The Cancer Imaging Archive (2017) (2017)

2. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Archive 286 (2017)

3. Bakas, S., et al.: Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data 4, 170117 (2017). https://doi.org/10.1038/sdata.2017.117 10.0.4.14/sdata.2017.117

4. Bakas, S., et al.: Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. CoRR abs/1811.0 (2018), http://arxiv.org/abs/1811.02629

5. Lecture Notes in Computer Science;W Chen,2019

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