Data augmentation for brain-tumor segmentation: a review
Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …
A comprehensive survey on brain tumor diagnosis using deep learning and emerging hybrid techniques with multi-modal MR image
The brain tumor is considered the deadly disease of the century. At present, neuroscience
and artificial intelligence conspire in the timely delineation, detection, and classification of …
and artificial intelligence conspire in the timely delineation, detection, and classification of …
Cross-modality deep feature learning for brain tumor segmentation
Recent advances in machine learning and prevalence of digital medical images have
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
opened up an opportunity to address the challenging brain tumor segmentation (BTS) task …
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
Attention gate resU-Net for automatic MRI brain tumor segmentation
Brain tumor segmentation technology plays a pivotal role in the process of diagnosis and
treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as …
treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as …
Exploring task structure for brain tumor segmentation from multi-modality MR images
Brain tumor segmentation, which aims at segmenting the whole tumor area, enhancing
tumor core area, and tumor core area from each input multi-modality bio-imaging data, has …
tumor core area, and tumor core area from each input multi-modality bio-imaging data, has …
Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field
Accurate segmentation of brain tumor is an indispensable component for cancer diagnosis
and treatment. In this paper, we propose a novel brain tumor segmentation method based …
and treatment. In this paper, we propose a novel brain tumor segmentation method based …
HDC-Net: Hierarchical decoupled convolution network for brain tumor segmentation
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 …
clinical treatment decision and surgical planning. Due to the large diversity of the tumors and …
GCAUNet: A group cross-channel attention residual UNet for slice based brain tumor segmentation
Z Huang, Y Zhao, Y Liu, G Song - Biomedical Signal Processing and …, 2021 - Elsevier
Precise brain tumor segmentation can improve patient prognosis. However, due to the
complicated structure of the human brain, brain tumor segmentation is a challenging task. To …
complicated structure of the human brain, brain tumor segmentation is a challenging task. To …
Mutual ensemble learning for brain tumor segmentation
It is challenging to reduce the generalization errors of brain tumor segmentation models on
test data, as the nature of the high diversity of tumors. The model ensemble combining …
test data, as the nature of the high diversity of tumors. The model ensemble combining …