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 …
[HTML][HTML] Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions
Removing the bias and variance of multicentre data has always been a challenge in large
scale digital healthcare studies, which requires the ability to integrate clinical features …
scale digital healthcare studies, which requires the ability to integrate clinical features …
Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
Recently, deep learning has unlocked unprecedented success in various domains,
especially using images, text, and speech. However, deep learning is only beneficial if the …
especially using images, text, and speech. However, deep learning is only beneficial if the …
Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics
Radiomics relies on the extraction of a wide variety of quantitative image-based features to
provide decision support. Magnetic resonance imaging (MRI) contributes to the …
provide decision support. Magnetic resonance imaging (MRI) contributes to the …
Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification
Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance
imaging (MRI) is an essential procedure in the field of medical imaging analysis for full …
imaging (MRI) is an essential procedure in the field of medical imaging analysis for full …
Brain tumor segmentation using convolutional neural networks in MRI images
Among brain tumors, gliomas are the most common and aggressive, leading to a very short
life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the …
life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the …
A survey of MRI-based brain tumor segmentation methods
Brain tumor segmentation aims to separate the different tumor tissues such as active cells,
necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM) …
necrotic core, and edema from normal brain tissues of White Matter (WM), Gray Matter (GM) …
DeepHarmony: A deep learning approach to contrast harmonization across scanner changes
Magnetic resonance imaging (MRI) is a flexible medical imaging modality that often lacks
reproducibility between protocols and scanners. It has been shown that even when care is …
reproducibility between protocols and scanners. It has been shown that even when care is …
Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks
The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS)
patients. Segmentation of the spinal cord and lesions from MRI data provides measures of …
patients. Segmentation of the spinal cord and lesions from MRI data provides measures of …
Quantitative phase imaging and artificial intelligence: a review
Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have
opened up the possibility of an exciting frontier. The fast and label-free nature of QPI …
opened up the possibility of an exciting frontier. The fast and label-free nature of QPI …