A novel end-to-end brain tumor segmentation method using improved fully convolutional networks
Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an
active research topic in medical image analysis since it provides doctors with meaningful …
active research topic in medical image analysis since it provides doctors with meaningful …
Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks
Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult,
tedious, and time-consuming task. The accuracy and the robustness of brain tumor …
tedious, and time-consuming task. The accuracy and the robustness of brain tumor …
Brain tumor segmentation using an adversarial network
Z Li, Y Wang, J Yu - Brainlesion: Glioma, Multiple Sclerosis, Stroke and …, 2018 - Springer
Recently, the convolutional neural network (CNN) has been successfully applied to the task
of brain tumor segmentation. However, the effectiveness of a CNN-based method is limited …
of brain tumor segmentation. However, the effectiveness of a CNN-based method is limited …
Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review
Brain is an amazing organ that controls all activities of a human. Any abnormality in the
shape of anatomical regions of the brain needs to be detected as early as possible to reduce …
shape of anatomical regions of the brain needs to be detected as early as possible to reduce …
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 …
Dual-force convolutional neural networks for accurate brain tumor segmentation
Brain tumor segmentation from Magnetic Resonance Imaging scans is vital for both the
diagnosis and treatment of brain cancers. It is widely accepted that accurate segmentation …
diagnosis and treatment of brain cancers. It is widely accepted that accurate segmentation …
Deep learning for segmentation of brain tumors: Impact of cross‐institutional training and testing
Background and purpose Convolutional neural networks (CNN s) are commonly used for
segmentation of brain tumors. In this work, we assess the effect of cross‐institutional training …
segmentation of brain tumors. In this work, we assess the effect of cross‐institutional training …
Review of MRI-based brain tumor image segmentation using deep learning methods
Brain tumor segmentation is an important task in medical image processing. Early diagnosis
of brain tumors plays an important role in improving treatment possibilities and increases the …
of brain tumors plays an important role in improving treatment possibilities and increases the …
Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy
In this paper, we present a new Deep Convolutional Neural Networks (CNNs) dedicated to
fully automatic segmentation of Glioblastoma brain tumors with high-and low-grade. The …
fully automatic segmentation of Glioblastoma brain tumors with high-and low-grade. The …
Glioma segmentation with cascaded UNet
D Lachinov, E Vasiliev, V Turlapov - International MICCAI Brainlesion …, 2018 - Springer
MRI analysis takes central position in brain tumor diagnosis and treatment, thus its precise
evaluation is crucially important. However, its 3D nature imposes several challenges, so the …
evaluation is crucially important. However, its 3D nature imposes several challenges, so the …