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 …
[HTML][HTML] Current applications of deep-learning in neuro-oncological MRI
Abstract Purpose Magnetic Resonance Imaging (MRI) provides an essential contribution in
the screening, detection, diagnosis, staging, treatment and follow-up in patients with a …
the screening, detection, diagnosis, staging, treatment and follow-up in patients with a …
Efficient framework for brain tumor detection using different deep learning techniques
The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are
classified using a biopsy, which is normally performed after the final brain surgery. Deep …
classified using a biopsy, which is normally performed after the final brain surgery. Deep …
Automatic and accurate abnormality detection from brain MR images using a novel hybrid UnetResNext-50 deep CNN model
The automatic and accurate detection and segmentation of brain tumors is a very tedious
and challenging task for medical experts and radiologists. This paper proposes a hybrid …
and challenging task for medical experts and radiologists. This paper proposes a hybrid …
[HTML][HTML] Pseudonymisation of neuroimages and data protection: Increasing access to data while retaining scientific utility
For a number of years, facial features removal techniques such as 'defacing','skull
stripping'and 'face masking/blurring', were considered adequate privacy preserving tools to …
stripping'and 'face masking/blurring', were considered adequate privacy preserving tools to …
Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre-and post-operative glioblastoma patients
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation
of treatment response for glioblastoma. This assessment is, however, complex to perform …
of treatment response for glioblastoma. This assessment is, however, complex to perform …
Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features
Hepatic cirrhosis is an increasing cause of mortality in developed countries—it is the
pathological sequela of chronic liver diseases, and the final liver fibrosis stage. Since …
pathological sequela of chronic liver diseases, and the final liver fibrosis stage. Since …
Segmenting pediatric optic pathway gliomas from MRI using deep learning
J Nalepa, S Adamski, K Kotowski… - Computers in Biology …, 2022 - Elsevier
Optic pathway gliomas are low-grade neoplastic lesions that account for approximately 3–
5% of brain tumors in children. Assessing tumor burden from magnetic resonance imaging …
5% of brain tumors in children. Assessing tumor burden from magnetic resonance imaging …
Deep learning with multiresolution handcrafted features for brain MRI segmentation
The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo
computed tomography (CT) images which are used to achieve positron emission …
computed tomography (CT) images which are used to achieve positron emission …
CNN-based deep learning technique for the brain tumor identification and classification in MRI images
A brain tumor is an abnormal development of cells in the brain that are either benign or
malignant. Magnetic resonance imaging (MRI) is used to identify tumors. Manual evaluation …
malignant. Magnetic resonance imaging (MRI) is used to identify tumors. Manual evaluation …