1. Aziz M. J., Tehrani zade A. A., Farnia P, Alimohamadi M, Makkiabadi B, Ahmadian A, Alirezaie J (2021). Accurate automatic glioma segmentation in brain mri images based on capsnet. bioRxiv
2. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann J, Farahani K, Davatzikos C (2017a) Segmentation labels and radiomic features for the pre-operative scans of the tcga-gbm collection. The cancer imaging archive. Nat Sci Data 4:170117
3. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J. S., Freymann J, Farahani K, Davatzikos C (2017b). Segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection. The cancer imaging archive
4. Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C (2017c) Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Nat Sci Data 4:170117
5. Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, Shinohara R. T., Berger C, Ha S. M., Rozycki M, et al (2018). 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