An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review
Background Artificial intelligence (AI) has become a prominent technique for medical
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
diagnosis and represents an essential role in detecting brain tumors. Although AI-based …
[HTML][HTML] A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions
B Pandey, DK Pandey, BP Mishra… - Journal of King Saud …, 2022 - Elsevier
The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
Brain tumor segmentation with deep convolutional symmetric neural network
Gliomas are the most frequent primary brain tumors, which have a high mortality. Surgery is
the most commonly used treatment. Magnetic resonance imaging (MRI) is especially useful …
the most commonly used treatment. Magnetic resonance imaging (MRI) is especially useful …
A sequential machine learning-cum-attention mechanism for effective segmentation of brain tumor
Magnetic resonance imaging is the most generally utilized imaging methodology that
permits radiologists to look inside the cerebrum using radio waves and magnets for tumor …
permits radiologists to look inside the cerebrum using radio waves and magnets for tumor …
Application of convolutional neural network in segmenting brain regions from MRI data
Extracting knowledge from digital images largely depends on how well the mining
algorithms can focus on specific regions of the image. In multimodality image analysis …
algorithms can focus on specific regions of the image. In multimodality image analysis …
MRI brain tumor medical images analysis using deep learning techniques: a systematic review
The substantial progress of medical imaging technology in the last decade makes it
challenging for medical experts and radiologists to analyze and classify. Medical images …
challenging for medical experts and radiologists to analyze and classify. Medical images …
Automated meningioma segmentation in multiparametric MRI: comparable effectiveness of a deep learning model and manual segmentation
KR Laukamp, L Pennig, F Thiele, R Reimer… - Clinical …, 2021 - Springer
Purpose Volumetric assessment of meningiomas represents a valuable tool for treatment
planning and evaluation of tumor growth as it enables a more precise assessment of tumor …
planning and evaluation of tumor growth as it enables a more precise assessment of tumor …
AdaptAhead optimization algorithm for learning deep CNN applied to MRI segmentation
Deep learning is one of the subsets of machine learning that is widely used in artificial
intelligence (AI) field such as natural language processing and machine vision. The deep …
intelligence (AI) field such as natural language processing and machine vision. The deep …
[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 …
Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning
L Zhou, Q Ji, H Peng, F Chen, Y Zheng, Z Jiao… - European …, 2024 - Springer
Objectives To develop a dynamic nomogram containing radiomics signature and clinical
features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and …
features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and …