Brain Tumor Segmentation and Survival Prediction Using Automatic Hard Mining in 3D CNN Architecture

Author:

Anand Vikas KumarORCID,Grampurohit Sanjeev,Aurangabadkar Pranav,Kori AvinashORCID,Khened Mahendra,Bhat Raghavendra S.,Krishnamurthi GanapathyORCID

Publisher

Springer International Publishing

Reference20 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, vol. 286 (2017)

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

3. Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)

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. arXiv preprint arXiv:1811.02629 (2018)

5. Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)

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