[HTML][HTML] Brain tumor analysis empowered with deep learning: A review, taxonomy, and future challenges
Deep Learning (DL) algorithms enabled computational models consist of multiple
processing layers that represent data with multiple levels of abstraction. In recent years …
processing layers that represent data with multiple levels of abstraction. In recent years …
RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames
Brain tumors are one of the most fatal cancers. Magnetic Resonance Imaging (MRI) is a non-
invasive method that provides multi-modal images containing important information …
invasive method that provides multi-modal images containing important information …
Brain tumour image segmentation using deep networks
Automated segmentation of brain tumour from multimodal MR images is pivotal for the
analysis and monitoring of disease progression. As gliomas are malignant and …
analysis and monitoring of disease progression. As gliomas are malignant and …
Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review
Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for …
[HTML][HTML] The virtual skeleton database: an open access repository for biomedical research and collaboration
M Kistler, S Bonaretti, M Pfahrer, R Niklaus… - Journal of medical …, 2013 - jmir.org
Background Statistical shape models are widely used in biomedical research. They are
routinely implemented for automatic image segmentation or object identification in medical …
routinely implemented for automatic image segmentation or object identification in medical …
Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient
S Abbasi, F Tajeripour - Neurocomputing, 2017 - Elsevier
Brain tumor pathology is one of the most common mortality issues considered as an
essential priority for health care societies. Accurate diagnosis of the type of disorder is …
essential priority for health care societies. Accurate diagnosis of the type of disorder is …
[HTML][HTML] A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery
Y Liu, S Stojadinovic, B Hrycushko, Z Wardak, S Lau… - PloS one, 2017 - journals.plos.org
Accurate and automatic brain metastases target delineation is a key step for efficient and
effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a …
effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a …
Brain tumor classification using the fused features extracted from expanded tumor region
In this study, a brain tumor classification method using the fusion of deep and shallow
features is proposed to distinguish between meningioma, glioma, pituitary tumor types and …
features is proposed to distinguish between meningioma, glioma, pituitary tumor types and …
[HTML][HTML] Medical big data: neurological diseases diagnosis through medical data analysis
Diagnosis of neurological diseases is a growing concern and one of the most difficult
challenges for modern medicine. According to the World Health Organisation's recent report …
challenges for modern medicine. According to the World Health Organisation's recent report …
Deep learning with mixed supervision for brain tumor segmentation
Most of the current state-of-the-art methods for tumor segmentation are based on machine
learning models trained manually on segmented images. This type of training data is …
learning models trained manually on segmented images. This type of training data is …