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 …
Transfer learning approaches for neuroimaging analysis: a scoping review
Deep learning algorithms have been moderately successful in diagnoses of diseases by
analyzing medical images especially through neuroimaging that is rich in annotated data …
analyzing medical images especially through neuroimaging that is rich in annotated data …
The medical segmentation decathlon
International challenges have become the de facto standard for comparative assessment of
image analysis algorithms. Although segmentation is the most widely investigated medical …
image analysis algorithms. Although segmentation is the most widely investigated medical …
ME‐Net: multi‐encoder net framework for brain tumor segmentation
W Zhang, G Yang, H Huang, W Yang… - … Journal of Imaging …, 2021 - Wiley Online Library
MRI plays a vital role to evaluate brain tumor diagnosis and treatment planning. However,
the manual segmentation of the MRI image is strenuous. With the development of deep …
the manual segmentation of the MRI image is strenuous. With the development of deep …
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 …
Overall survival prediction in glioblastoma with radiomic features using machine learning
Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of
patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) …
patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) …
Brain tumor segmentation using cascaded deep convolutional neural network
Gliomas are the most common and threatening brain tumors with little to no survival rate.
Accurate detection of such tumors is crucial for survival of the subject. Naturally, tumors have …
Accurate detection of such tumors is crucial for survival of the subject. Naturally, tumors have …
The University of California San Francisco preoperative diffuse glioma MRI dataset
E Calabrese, JE Villanueva-Meyer, JD Rudie… - Radiology: Artificial …, 2022 - pubs.rsna.org
The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset |
Radiology: Artificial Intelligence RSNA "skipMainNavigation" closeDrawerMenuopenDrawerMenu …
Radiology: Artificial Intelligence RSNA "skipMainNavigation" closeDrawerMenuopenDrawerMenu …
Explainability of deep neural networks for MRI analysis of brain tumors
Purpose Artificial intelligence (AI), in particular deep neural networks, has achieved
remarkable results for medical image analysis in several applications. Yet the lack of …
remarkable results for medical image analysis in several applications. Yet the lack of …
Region-based evidential deep learning to quantify uncertainty and improve robustness of brain tumor segmentation
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …
from low reliability and robustness. Uncertainty estimation is an efficient solution to this …