Brain tumor segmentation with missing modalities via latent multi-source correlation representation
Multimodal MR images can provide complementary information for accurate brain tumor
segmentation. However, it's common to have missing imaging modalities in clinical practice …
segmentation. However, it's common to have missing imaging modalities in clinical practice …
Brain tumor segmentation using K‐means clustering and deep learning with synthetic data augmentation for classification
Image processing plays a major role in neurologists' clinical diagnosis in the medical field.
Several types of imagery are used for diagnostics, tumor segmentation, and classification …
Several types of imagery are used for diagnostics, tumor segmentation, and classification …
Segmentation of brain tumors and patient survival prediction: Methods for the brats 2018 challenge
Brain tumor localization and segmentation is an important step in the treatment of brain
tumor patients. It is the base for later clinical steps, eg, a possible resection of the tumor …
tumor patients. It is the base for later clinical steps, eg, a possible resection of the tumor …
One-pass multi-task networks with cross-task guided attention for brain tumor segmentation
Class imbalance has emerged as one of the major challenges for medical image
segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates …
segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates …
Automatic brain tumor segmentation using convolutional neural networks with test-time augmentation
Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning
and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) …
and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) …
Brain tumor segmentation using deep learning by type specific sorting of images
Z Sobhaninia, S Rezaei, A Noroozi, M Ahmadi… - arXiv preprint arXiv …, 2018 - arxiv.org
Recently deep learning has been playing a major role in the field of computer vision. One of
its applications is the reduction of human judgment in the diagnosis of diseases. Especially …
its applications is the reduction of human judgment in the diagnosis of diseases. Especially …
[HTML][HTML] Demystifying brain tumor segmentation networks: interpretability and uncertainty analysis
The accurate automatic segmentation of gliomas and its intra-tumoral structures is important
not only for treatment planning but also for follow-up evaluations. Several methods based on …
not only for treatment planning but also for follow-up evaluations. Several methods based on …
Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation
We introduce a new family of classifiers based on our previous DeepSCAN architecture, in
which densely connected blocks of dilated convolutions are embedded in a shallow U-net …
which densely connected blocks of dilated convolutions are embedded in a shallow U-net …
Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario
Purpose Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has
wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence …
wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence …
An end‐to‐end brain tumor segmentation system using multi‐inception‐UNET
Accurate detection and pixel‐wise classification of brain tumors in Magnetic Resonance
Imaging (MRI) scans are vital for their diagnosis, prognosis study and treatment planning …
Imaging (MRI) scans are vital for their diagnosis, prognosis study and treatment planning …