Filters








5,013 Hits in 5.2 sec

Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review [article]

Jose Bernal, Kaisar Kushibar, Daniel S. Asfaw, Sergi Valverde, Arnau Oliver, Robert Martí, Xavier Lladó
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]

Adnan Qayyum, Syed Muhammad Anwar, Muhammad Majid, Muhammad Awais, Majdi Alnowami
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]

Mohammad Amin Morid, Alireza Borjali, Guilherme Del Fiol
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]

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
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

Satya P. Singh, Lipo Wang, Sukrit Gupta, Haveesh Goli, Parasuraman Padmanabhan, Balázs Gulyás
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

Xiangbin Liu, Liping Song, Shuai Liu, Yudong Zhang
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

N. Karthick
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]

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
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

Juan Miguel Valverde, Vandad Imani, Ali Abdollahzadeh, Riccardo De Feo, Mithilesh Prakash, Robert Ciszek, Jussi Tohka
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]

Reza Azad, Nika Khosravi, Mohammad Dehghanmanshadi, Julien Cohen-Adad, Dorit Merhof
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

Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
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

Haonan Xiao, Xinzhi Teng, Chenyang Liu, Tian Li, Ge Ren, Ruijie Yang, Dinggang Shen, Jing Cai
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

Tongxue Zhou, Su Ruan, Stéphane Canu
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]

Saeid Asgari Taghanaki, Kumar Abhishek, Joseph Paul Cohen, Julien Cohen-Adad, Ghassan Hamarneh
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

Shruti Agarwal, Department of Computer Science Engineering, BBD University, Lucknow, India
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
« Previous Showing results 1 — 15 out of 5,013 results