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Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review
[article]
2018
arXiv
pre-print
We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing ...
Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. ...
One of the most widely adopted approaches of deep neural networks is the convolutional neural networks which can process array-like data [40] , such as images or video sequences. ...
arXiv:1712.03747v3
fatcat:sq5nphm645hkliljzs74py4htm
Medical Image Analysis using Convolutional Neural Networks: A Review
[article]
2017
arXiv
pre-print
This paper presents a review of the state-of-the-art convolutional neural network based techniques used for medical image analysis. ...
Recently, deep learning methods utilizing deep convolutional neural networks have been applied to medical image analysis providing promising results. ...
In [44] , a method for detection of myocardial abnormalities is presented using cardiac magnetic resonance imaging. ...
arXiv:1709.02250v1
fatcat:mlk3vdn7ibggvcxzsb7c23kibq
A scoping review of transfer learning research on medical image analysis using ImageNet
[article]
2020
arXiv
pre-print
Objective: Employing transfer learning (TL) with convolutional neural networks (CNNs), well-trained on non-medical ImageNet dataset, has shown promising results for medical image analysis in recent years ...
Also, we identified several critical research gaps existing in the TL studies on medical image analysis. ...
IEEE ("transfer learning" OR "deep learning" OR "convolutional neural network" OR "convolutional neural networks") AND ( "MRI" OR "MRIs" OR "Magnetic resonance images" OR "Magnetic resonance image" OR ...
arXiv:2004.13175v5
fatcat:wqghyqq4wfgpnpatvftty4vzx4
3D Deep Learning on Medical Images: A Review
[article]
2020
arXiv
pre-print
This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. ...
In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. ...
Cardiac magnetic resonance (CMR) imaging is popular in diagnosing various cardiovascular diseases. ...
arXiv:2004.00218v4
fatcat:iucszcjffnbwbbzc4zzqpbvahy
3D Deep Learning on Medical Images: A Review
2020
Sensors
This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. ...
In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. ...
Cardiac magnetic resonance (CMR) imaging is popular in diagnosing various cardiovascular diseases. Vesel et al. ...
doi:10.3390/s20185097
pmid:32906819
pmcid:PMC7570704
fatcat:top2ambpizdzdpsqamz2xm643u
A Review of Deep-Learning-Based Medical Image Segmentation Methods
2021
Sustainability
With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. ...
Aiming at the above problems, a comprehensive review of current medical image segmentation methods based on deep learning is provided to help researchers solve existing problems. ...
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. ...
doi:10.3390/su13031224
fatcat:pn2qbyv53zbuhhiuem2pc4dg3u
A Review on Brain Tumour Detection using Magnetic Resonance Imaging
2019
International Journal for Research in Applied Science and Engineering Technology
Magnetic Resonance Imaging (MRI) method is the most famous non-intrusive strategy; in nowadays imaging of organic structures by MRI is a typical exploring system. ...
For segmentation, generally utilized clustering calculation like fluffy c-means, k-means a few specialists utilized convolution neural system approach and GPM. ...
Convolution neural networks is another methodology of profound neural calculation for segmentation. ...
doi:10.22214/ijraset.2019.10020
fatcat:o4ewlspozbdt5ddkh7yuczczg4
Deep learning for cardiac image segmentation: A review
[article]
2019
arXiv
pre-print
In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography ...
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. ...
Convolutional Neural Networks (CNNs) In this part, we will introduce convolutional neural network (CNN), which is the most common type of deep neural networks for image analysis. ...
arXiv:1911.03723v1
fatcat:cwsq5hiaebgkza5ktmtyw553je
Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review
2021
Journal of Imaging
The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). ...
In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. ...
For instance, Weninger et al. [20] proposed a multi-task autoencoder-like convolutional neural network with three decoders-one per task-to segment and reconstruct brain MR images containing tumors. ...
doi:10.3390/jimaging7040066
pmid:34460516
pmcid:PMC8321322
fatcat:qpqjwl4bybhsfd4vdsnt3vyyye
Medical Image Segmentation on MRI Images with Missing Modalities: A Review
[article]
2022
arXiv
pre-print
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging. ...
The compensation of the adverse impact of losing useful information owing to the lack of one or more modalities is a well-known challenge in the field of computer vision, particularly for medical image ...
Introduction Magnetic resonance imaging, widely known as MRI, is one of the most effective techniques used in biomedical imaging for obtaining high contrast images of the soft tissues in human body such ...
arXiv:2203.06217v1
fatcat:wbfhesrpajdy3pfqbnut6il32e
Deep Learning for Cardiac Image Segmentation: A Review
2020
Frontiers in Cardiovascular Medicine
In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography ...
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. ...
magnetic resonance (MR) images. ...
doi:10.3389/fcvm.2020.00025
pmid:32195270
pmcid:PMC7066212
fatcat:iw7xpnltn5cgbn5ullq2ldy3nq
A review of deep learning-based three-dimensional medical image registration methods
2021
Quantitative Imaging in Medicine and Surgery
Medical image registration is a vital component of many medical procedures, such as image-guided radiotherapy (IGRT), as it allows for more accurate dose-delivery and better management of side effects. ...
A summary is presented following a review of each category of study, discussing its advantages, challenges, and trends. ...
CNN, convolutional neural network; GAN, generative adversarial network. ...
doi:10.21037/qims-21-175
pmid:34888197
pmcid:PMC8611468
fatcat:jqa27ap7evgwvf3o4uy2vve57e
A review: Deep learning for medical image segmentation using multi-modality fusion
2019
Array
In this paper, we give an overview of deep learning-based approaches for multi-modal medical image segmentation task. ...
Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). ...
For example, Litjens et al. [44] reviewed the major deep learning concepts in medical image analysis. Bernal et al. [5] gave an overview in deep CNN for brain MRI analysis. ...
doi:10.1016/j.array.2019.100004
fatcat:rhtf2kr5obbabhawjbzilj5z3y
Deep Semantic Segmentation of Natural and Medical Images: A Review
[article]
2020
arXiv
pre-print
In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. ...
In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, ...
reviewed the literature on techniques to handle label noise in deep learning based medical image analysis and evaluated existing approaches on three medical imaging datasets for segmentation and classification ...
arXiv:1910.07655v3
fatcat:uxrrmb3jofcsvnkfkuhfwi62yq
A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis
2021
Journal of Informatics Electrical and Electronics Engineering (JIEEE)
This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. ...
Over the past 20 years, the global research going on in Artificial Intelligence in applications in medication is a venue internationally, for medical trade and creating an energetic research community. ...
The authors have proposed in [16] , a deep convolutional neural network (CNN) for Alzheimer's disease diagnosis using analysis of Magnetic Resonance Imaging data. ...
doi:10.54060/jieee/002.02.010
fatcat:gzfzcebf45bn3h5ljcyrbed5xu
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