[PDF] arxiv.org

Automated brain tumour segmentation using cascaded 3d densely-connected u-net

M Ghaffari, A Sowmya, R Oliver - … , Stroke and Traumatic Brain Injuries: 6th …, 2021 - Springer
Accurate brain tumour segmentation is a crucial step towards improving disease diagnosis
and proper treatment planning. In this paper, we propose a deep-learning based method to …
被引用次数：42 相关文章 所有 5 个版本

CLCU-Net: Cross-level connected U-shaped network with selective feature aggregation attention module for brain tumor segmentation

YL Wang, ZJ Zhao, SY Hu, FL Chang - Computer methods and programs in …, 2021 - Elsevier
Abstract Background and Objective Brain tumors are among the most deadly cancers
worldwide. Due to the development of deep convolutional neural networks, many brain …
被引用次数：28 相关文章 所有 4 个版本

ERV-Net: An efficient 3D residual neural network for brain tumor segmentation

X Zhou, X Li, K Hu, Y Zhang, Z Chen, X Gao - Expert Systems with …, 2021 - Elsevier
Brain tumors are the most aggressive and mortal cancers, which lead to short life
expectancy. A reliable and efficient automatic or semi-automatic segmentation method is …
被引用次数：104 相关文章

Current trends on deep learning models for brain tumor segmentation and detection–a review

S Somasundaram, R Gobinath - 2019 International conference …, 2019 - ieeexplore.ieee.org
Critical component in diagnosing tumor, designing treatment and developing an outcome for
evaluating brain tumor segmentation needed to be highly accurate and reliable. Magnetic …
被引用次数：58 相关文章
[PDF] researchgate.net

HDC-Net: Hierarchical decoupled convolution network for brain tumor segmentation

Z Luo, Z Jia, Z Yuan, J Peng - IEEE Journal of Biomedical and …, 2020 - ieeexplore.ieee.org
Accurate segmentation of brain tumor from magnetic resonance images (MRIs) is crucial for
clinical treatment decision and surgical planning. Due to the large diversity of the tumors and …
被引用次数：91 相关文章 所有 4 个版本