Multimodal MRI brain tumor segmentation using 3D attention UNet with dense encoder blocks and residual decoder blocks

Author:

Tassew Tewodros,Ashamo Betelihem Asfaw,Nie Xuan

Publisher

Springer Science and Business Media LLC

Reference62 articles.

1. Al-Qazzaz S (2020) Deep learning-based brain tumour image segmentation and its extension to stroke lesion segmentation. Diss. Cardiff University

2. Alagarsamy S, Zhang YD, Govindaraj V et al (2020) Smart identification of topographically variant anomalies in brain magnetic resonance imaging using a fish school-based fuzzy clustering approach[J]. IEEE Trans Fuzzy Syst 29(10):3165–3177

3. Azhari EEM, Hatta MM, Htike ZZ, Win SL (2014) Tumor detection in medical imaging: a survey. Int J Adv Inf Technol 4:21–30

4. Baid U, Talbar S, Rane S et al (2020) A novel approach for fully automatic intra-tumor segmentation with 3D U-Net architecture for gliomas[J]. Front Comput Neurosci 14:10

5. Ballestar LM, Vilaplana V (2020) Brain tumor segmentation using 3d-cnns with uncertainty estimation. arXiv preprint arXiv:2009.12188

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