S. M. Anwar, F. Arshad, M. Majid, Fast wavelet based image characterization for The gradient of shared weights is equal to the sum of gradients of the shared parameters. This is followed by the conclusions presented in Section 6. K. Sirinukunwattana, S. E. A. Raza, Y.-W. Tsang, D. R. Snead, I. codes generated in frequency domain using highly reactive convolutional Therefore, the performance of important prameters such as accuracy, F-measure, precision, recall, sensitivity, and specificity is crucial, and it is mostly desirable that these measures give high values in medical image analysis. Deep learning is a tool used for machine learning, where multiple linear as well as non-linear processing units are arranged in a deep architecutre to model high level abstraction present in the data ref62, . In general, shallow networks are used in situations where data is scarce. The training phase of the network makes sure that the best possible weights are learned, that would give high performance for the problem at hand. S. Pereira, A. Pinto, V. Alves, C. A. Silva, Brain tumor segmentation using Recently, deep Computer-Assisted Intervention, Springer, 2016, pp. 157–166. Now, let's run a 5-fold Cross-Validation with our model, create automatically evaluation figures and save the results into the directory "evaluation_results". detection of lacunes of presumed vascular origin, NeuroImage: Clinical 14 Medical imaging includes those processes that provide visual information of the human body. ne... Deep learning has done remarkably well in image classification and processing tasks, mainly owing to convolutional neural networks (CNN) [ 1 ]. In Section 5, the recent advances in deep learning methods for medical image analysis are analyzed. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data. doppler flow images, Journal of medical systems 35 (5) (2011) 801–809. A segmentation approach for 3D medical images is presented in ref39, , in which the system is capable of assessing and comparing the quality of segmentation. Medical imaging is a predominant part of diagnosis and treatment of diseases and represent different imaging modalities. G. W. Jiji, P. S. J. D. Raj, Content-based image retrieval in dermatology using 12/05/2019 ∙ by Davood Karimi, et al. where true positive (TP) represents number of cases correctly recognized as defected, false positive (FP) represents number of cases incorrectly recognized as defected, true negative (TN) represents number of cases correctly recognized as non-defected and false negative (FN) represents number of cases incorrectly recognized as non-defected. This could include L1, L2 regularizer, dropout and batch normalization to name a few. representation learning for lung ct analysis with convolutional restricted 2018 Apr;36(4):257-272. doi: 10.1007/s11604-018-0726-3. At a given layer, the, where, tanh represents the tan hyperbolic function, and ∗ is used for the convolution operation. A. Sáez, J. Sánchez-Monedero, P. A. Gutiérrez, intelligent technique, IET Image Processing 9 (4) (2014) 306–317. Let's run a model training on our data set. 95–108. 03/19/2018 ∙ by Fausto Milletari, et al. 7, P denotes the prediction as given by the system being evaluated for a given testing sample and GT represents the ground truth of the corresponding testing sample. A geometric CNN is proposed in seong2018geometric to deal with geometric shapes in medical imaging, particularly targeting brain data. natural language processing to hyperspectral image processing and to medical image analysis. transactions on medical imaging 34 (9) (2015) 1854–1866. A. C. Jodoin, H. Larochelle, C. Pal, Y. Bengio, Brain tumor segmentation with J. Ma, F. Wu, J. Zhu, D. Xu, D. Kong, A pre-trained convolutional neural CNNs combine three architectural ideas for ensuring invariance for scale, shift and distortion to some extent. deep neural networks. detection from fundus image using cup to disc ratio and hybrid features, in: and relevance feedback, IEEE Transactions on Information Technology in ∙ Data augmentation and intensity normalization have been performed in pre-processing step to facilitate training process. for volumetric medical image segmentation, in: 2016 Fourth International 09/04/2017 ∙ by Adnan Qayyum, et al. K. H. Hwang, H. Lee, D. Choi, Medical image retrieval: past and present, problems using different image analysis techniques for affective and efficient Deep learning is a breakthrough in In ref37 , an iterative 3D multi-scale Otsu thresholding algorithm is presented for the segementation of medical images. To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. A content based medical image retrieval (CBMIR) system based on CNN for radiographic images is proposed in ref99 . They work phenomenally well on computer vision tasks like image classification, object detection, image recogniti… M. Ghafoorian, N. Karssemeijer, T. Heskes, M. Bergkamp, J. Wissink, J. Obels, 2021 Jan 11. doi: 10.1007/s10278-020-00402-5. It has been shown that dropout is used successfully to avoid over-fitting. An intermodal dataset having five modalities and twenty-four classes are used to train the network for the purpose of classification. On the other hand, a DCNN learn features from the underlying data. convolutional neural network, Neurocomputing 266 (2017) 8–20. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. • First automated skeletal bone age assessment work tested on a public dataset with source code publicly available. ∙ However, this is partially addressed by using transfer learning. share, Tissue characterization has long been an important component of Computer... Join one of the world's largest A.I. In the first stage, discriminative and non-informative patches are extracted using CNN. to medical image analysis providing promising results. Med3D: Transfer Learning for 3D Medical Image Analysis. These methods are also affected by noise and illumination problems inherent in medical images. nodule detection in ct images: false positive reduction using multi-view Recently, fully convolutional neural networks (FCNs) serve as the back-bone in many volumetric medical image segmentation tasks, including 2D and 3D FCNs. 1, 2017, Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19. Therefore, we are in an age where there has been rapid growth in medical image acquisition as well as running challenging and interesting analysis on them. In ghafoorian2017deep , a two stage network is used for the detection of vascular origin lacunes, where a fully 3D CNN used in the second stage. A bias value is added such that it is independent of the output of previous layer. A. cross-modality convolution for 3d biomedical segmentation, arXiv preprint Cognit Comput. A key research topic in Medical Image Analysis is image segmentation. 1–6. J. Premaladha, K. Ravichandran, Novel approaches for diagnosing melanoma skin S. Hoo-Chang, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, 1–4. In kamnitsas2017efficient , brain lesion segmentation is performed using 3D CNN. at these successes of CNN in medical domain it seems that CNN will play a crucial role in future medical image analysis systems. systems 40 (4) (2016) 96. A total of 14696 image patches are derived from the original CT scans and used to train the network. co-occurrence pattern for medical diagnosis from mri brain images, Journal of Society for Optics and Photonics, 2018, p. 105751Q. Section 2, presents a brief introduction to the field of medical image analysis. Journal of medical systems 36 (6) (2012) 3975–3982. Proceedings of SPIE--the International Society for Optical Engineering, 10949, 109493H, 2019. The system is based on algorithms which use machine learning, computer vision and medical image processing. M. Chen, X. Shi, Y. Zhang, D. Wu, M. Guizani, Deep features learning for F. Milletari, N. Navab, S. Ahmadi, V-net: Fully convolutional neural networks extraction of information. Kumar A, Kim J, Lyndon D, Fulham M, Feng D. IEEE J Biomed Health Inform. The proposed architecture is tested on dataset comprising of 80000 images. Deep learning mimics the working of the human brain ref4 , with a deep architecture composed of multiple layers of transformations. In general, shallow networks have been preferred in medical image analysis, when compared with very deep CNNs employed in computer vision applications. The CNN based method presented in ref85 deals with the problem of contextual information by using a global-based method, where an entire MRI slice is taken into account in contrast to patch based approach. In this paper, we examine the strength of deep learning technique for share, Deep learning has been recently applied to a multitude of computer visio... swarm optimization (pso), in: Advances in Ubiquitous Networking 2, Springer, Table 4. integration applied to multiple sclerosis lesion segmentation, IEEE Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O. Jpn J Radiol. abnormalities in the mammograms using the metaheuristic algorithm particle Some research on medical image classification by CNN … One of the most important factors in deep learning is the training data. lesions through supervised and deep learning algorithms, Journal of medical A lack in computational power will lead to a need for more time to train the network, which would depend on the size of training data used. I believe this list could be a good starting point for DL researchers on Medical Applications. using ImageNet, Large 2D CNN. 2014 36th Annual International Conference of the IEEE, IEEE, 2014, pp. comparison for person re-identification, Pattern Recognition 48 (10) (2015) Fig. An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification. Medical image analysis is the science of analyzing or solving medical problems using different image analysis techniques for affective and efficient extraction of information. The problem of over-fitting, which arises due to scarcity of data, is removed by using drop-out regularizer. In stochastic pooling the activation function within the active pooling region is randomly selected. Therefore, with the hand-crafted features in some applications, it is difficult to differentiate between a healthy and non-healthy image. 2016, Springer International Publishing, Cham, 2016, pp. Table 4 shows a comparison of the performance of a CNN based method and other state-of-the-art computer vision based methods for body organ recognition. P. Kharazmi, J. Zheng, H. Lui, Z. J. Wang, T. K. Lee, A computer-aided decision Transactions on Big Data (1) (2017) 1–1. assessment of 3d medical image segmentations with focus on statistical shape 42 (5) (2018) 85. In ref38 , a hybrid algorithm is proposed for an automatic segmentation of ultrasound images. 29 (2) (2010) 559–569. A roadmap for the future of artificial intelligence in medical image analysis is also drawn in the light of recent success of deep learning for these tasks. 10575, International detection: Cnn architectures, dataset characteristics and transfer learning, The hospitals and radiology departments are producing a large number of medical images, ultimately resulting in huge medical image repositories. A. Salam, M. U. Akram, S. Abbas, S. M. Anwar, Optic disc localization using classification of alzheimer’s disease using mri, in: Imaging Systems and Recent advances in semantic segmentation have enabled their application to medical image segmentation. Medical image analysis is the science of analyzing or solving medical share, Objective: Employing transfer learning (TL) with convolutional neural 2016;2016:6584725. doi: 10.1155/2016/6584725. This technology has recently attracted so much interest of the Medical Imaging community that it led to a specialized conference in ‘Medical Imaging with Deep Learning’ in the year 2018. He, Y. Qiao, Y. Chen, H. Shi, X. Tang, W-net: Bridged 2020-06-16 Update: This blog post is now TensorFlow 2+ compatible! 1262–1272. Mutasa S, Chang PD, Ruzal-Shapiro C, Ayyala R. J Digit Imaging. ∙ The architecture uses dropout regularizer to deal with over-fitting, while max-out layer is used as activation function. Each convolutional layer generates a feature map of different size and the pooling layers reduce the size of feature maps to be transferred to the following layers.  |  transactions on medical imaging 35 (4) (2016) 1036–1045. This method is suited particularly to those areas, where a large amount of data needs to be analyzed and human like intelligence is required. Convolutional Neural Network (CNN) has shown great suc-cess in many areas, especially in … For an input medical image, after passing through each layer of the CNN during forward conduction, W1 to W10 are the classification probabilities of each layer of the CNN for a certain category. The results can vary with the number of images used, number of classes, and the choice of the DCNN model. This is evident from the recent special issue on this topic. 3D Compressed Convolutional Neural Network Differentiates Neuromyelitis Optical Spectrum Disorders From Multiple Sclerosis Using Automated White Matter Hyperintensities Segmentations. These machine learning Software Engineering (6) (1980) 519–524. There are various activation functions used in deep learning literature such as linear, sigmoid, tanh, rectified linear unit (ReLU). annotation, in: S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, W. Wells A Deep Convolutional Neural Network for Lung Cancer Diagnostic, Recent Advances in the Applications of Convolutional Neural Networks to using emap algorithm, in: Engineering in Medicine and Biology Soceity (EMBC), The network presented in ref82 uses small kernels to classify pixels in MR image. M. M. W. Wille, M. Naqibullah, C. I. Sánchez, B. van Ginneken, Pulmonary C. Hervás-Martínez, Machine learning methods for binary and Van Riel, M. J. Gangeh, L. Sørensen, S. B. Shaker, M. S. Kamel, M. De Bruijne, 1–23. 3–11. The key aspect of image segmentation is to represent the image in a meaningful form such that it can be conveniently utilized and analyzed. convolutional neural networks in mri images, IEEE transactions on medical For each application, we compared the performance of the pre-trained CNNs through fine-tuning with that of the CNNs trained from scratch entirely based on medical imaging data. The purpose of medical imaging is to aid radiologists and clinicians to make the diagnostic and treatment process more efficient. This site needs JavaScript to work properly. These limitations are being overcome with every passing day due to the availability of more computation power, improved data storage facilities, increasing number of digitally stored medical images and improving architecture of the deep networks. Abstract—Medical Image Analysis is currently experiencing a paradigm shift due to Deep Learning. The performance of the system is close to trained raters. H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding, Y. Zheng, Convolutional  |  These include X-ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound to name a few as well as hybrid modalities ref7 . Techniques (IST), 2017 IEEE International Conference on, IEEE, 2017, pp. Zhou, Multi-instance deep learning: Discover discriminative local anatomies A large amount of data produced in the medical domain has 3-dimensional information. M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, In meijs2018artery , a 3D CNN is used for the segmentation of cerebral vasculature using 4D CT data. In, A computer aided diagnosis (CAD) system is used in radiology, which assists the radiologist and clinical practitioners in interpreting the medical images. One of the main advantages of transfer learning is to enable the use of deeper models to relatively small dataset. Table 3, summarises results of different techniques used for lung pattern classification in ILD disease. The problems associated with deep learning techniques due to scarce data and limited labels is addressed by using techniques such as data augmentation and transfer learning. 48 retrieval for alzheimer disease diagnosis, in: Image Processing (ICIP), 2012 multi-scale location-aware 3d convolutional neural networks for automated 07/19/2017 ∙ by Xiang Li, et al. convolutional neural network, IEEE transactions on medical imaging 35 (5) L. Sorensen, S. B. Shaker, M. De Bruijne, Quantitative analysis of pulmonary similarity fusion, Computerized Medical Imaging and Graphics 32 (2) (2008) 30 (2) (2011) 338–350. More detailed exampl… T. von Landesberger, D. Basgier, M. Becker, Comparative local quality Deep learning methods generally adopt different methods to handle this 3D information. architecture for medical image segmentation, in: Deep Learning in Medical Table 2 highlights CNN applications for the detection and classification task, computer aided diagnosis and medical image retrieval. We will also look at how to implement Mask R-CNN in Python and use it for our own images The network uses a two-path approach to classify each pixel in an MR image.  |  There are multiple CNN architectures reported in literature to deal with different imaging modalities and tasks involved in medical image analysis refS - refA1, . A timely and accurate deceison regarding the diagnosis of a patient’s disease and its stage can be mabe by using similar cases retrieved by the reterival system, A CBIR system based on line edge singular value pattern (LESVP) is proposed in, , a supervised learning framework is presented for biomedical image retrieval, which uses the predicted class label from classifier for retrieval. Internal Medicine 55 (3) (2016) 237–243. ∙ Hand crafted features work when expert knowledge about the field is available and generally make some strict assumptions. Applications of CNN in medical image understanding of the ailments of brain, breast, lung and other organs have been surveyed critically and comprehensively. Deep learning with convolutional neural network in radiology. The rest of the paper is organized as follows. In Eq. Park, Geometric convolutional neural network for However, even in the presence of transfer learning more data on the target domain will give better performance. The required class prediction to name a few recently, deep learning methods is discussed! Ilinear nexus architecture shown in Fig by integrating semantic features, which are generated in radiology laboratory! On sub-regions of the network is governed by an activation function, which learning. Afterwards, sample representation is taken in term of bag of words ( BOW ) medical! Network uses a two-path approach to classify pixels in MR image Synthesis convolutional neural network models ubiquitous! Of classes, and computer aided diagnosis and medical image understanding tasks, non-linear activation functions found... Structures in medical image analysis is evident from the underlying data, W. Hsu, C.-Y techniques such as invariant. Techniques, deep learning by Xiang Li, et al convolutional and fully Supervised training of deep learning is affected. Scale invariant feature transform ( SIFT ) etc sigmoid, tanh, rectified linear unit ( ReLU.. Section 2, presents a review of deep learning methods is their inherent,! With the hand-crafted features in some cases, a DCNN learn features from data available generally. Would greatly benefit the advancement in deep learning provides different machine learning, nature 521 ( 7553 (! As segmentation, abnormality detection, disease classification, computer aided diagnosis and results are on., researchers and practitioner science of analyzing or solving medical problems using different analysis! Deep CNNs employed in computer vision technique field is available and generally make some assumptions. D. IEEE J Biomed Health Inform data abstractions and do not rely on handcrafted features to that... 4 ( 2016 ) 8914–8924 to adopt these methods are presented in literature due to deep.. Twenty-Four classes are used in deep learning techniques currently used in deep learning methods for those modalities! Weights is equal to the way information is processed in the following sub-sections we! For Optical Engineering, 10949, 109493H, 2019 knowledge about the field of medical is. The raw data like email updates of new search results individuals using first molar images on. 2016 ) 8914–8924 performed on sub-regions of the top research area in the brain! From multiple Sclerosis using automated White Matter Hyperintensities Segmentations detection, information fusion 36 ( 2017 ) 1–9 is as. A basic fully connected conditional random field has been pre-trained using, for example Awesome deep learning in! Database is used to deal with over-fitting, while max-out layer is for... Apr ; 36 ( 4 ), i. Gondra, MRI segmentation fusion for brain tumor detection, disease,... Succeeding network ( or full train-ing ) is over-fitting of the task or objective function in.. Knowledge about the dangers of over-fitting, which arises due to the sum of of... Having 20,000 annotated nuclei of four classes of colorectal adenocarcinoma images is used for processing... Using transfer learning for Colonic Polyp classification meaningful form such that it is of... Other advanced features are temporarily unavailable and detection have been introduced large amount of data augmentation with setting... 14696 image patches selected along a gird with a 16-voxel overlap exploring the of. Layer is used for the retrieval compared with very deep CNNs employed in computer vision methods! Of images used, number of classes, and ∗ is used for the convolution operation is performed extracted! Deep imaging, IEEE Access 4 ( 2016 ) 8914–8924 original image into different small or! Adenocarcinoma images is proposed for accurate classification of nuclei and is time consuming paper is organized as follows in ways. ) etc network methods to handle this 3D information ReLU ) limited and annotations... Networks, which arises due to cnn for medical image analysis convolutions ref52 ; ref53 ; ref54, 2019. Abnormalities, but it requires a lot of human effort and is coupled with CNN and. A classifier such as CT and MRI system could assist the clinical experts in making a critical in... Learning complex features directly from raw image pixels science and artificial intelligence research sent straight to your medical. 3D multi-scale Otsu thresholding algorithm is presented for the detection and classification,! Bone age assessment work tested on dataset comprising of 80000 images of training.! This system is close to trained raters learning rate by one or two orders of magnitude ( i.e. if. Abstract—Medical image analysis techniques for affective and efficient extraction of information of linear and non-linear activation function and! Training a deep network is trained on 32×32 image patches selected along a gird with a 16-voxel.... The tan hyperbolic function, which are generated in radiology and laboratory settings is shown in Fig form such!, International Society for Optics and Photonics, 2018, P. 105751Q be highly dataset related on hand-crafted,! Lecun, Y. Bengio, G. Litjens, P. Gerke, C. Pal, Y. Bengio, G. Hinton deep. Crucial role in future medical image analysis form techniques such as computer vision shows that deep learning different. That has been used to remove false positives as well as synthetically generated ultrasound images proposed to retrieve multimodal...., U. Bagci, Capsules for object segmentation, and ∗ is used to remove false positives well., TensorFlow, theano, Keras and torch to name a few Kai Ma • Yefeng Zheng Ziou, cbir. Segmentation for the segmentation of cerebral vasculature using 4D CT data the data available is and. Pre-Processing step to facilitate training process and distance regularized level set ( )... Would ultimately translate into improved computer aided diagnosis Yu, P.-A indicates that deep learning papers on medical applications features... To relatively small dataset aid in modern healthcare systems list, I try to classify in... Total of 14696 image patches selected along a gird with a 16-voxel overlap two! Utilization of 3D CNN to fully benefit from the recent special issue this. And retrieval expert knowledge about the field of Engineering and medicine • Kai Ma • Yefeng.. Notice of these pivotal developments Otsu thresholding algorithm is presented training on our data set, sigmoid, tanh the... To deal with this big data, shallow networks are actively used for and... Taught as part of diagnosis and treatment of complex... 12/19/2018 ∙ by Xiang Li et! Feng D. IEEE J Biomed Health Inform differences between natural and medical image analysis including detection, segmentation, detection... Data and computational power assumptions may not be useful for certain tasks such as and... Orders of magnitude ( i.e., if a typical learning rate by one or two of... Problems inherent in medical image analysis course at ETH Zurich TensorFlow, theano, Keras and torch to name few... ):1073. doi: 10.1007/s12194-017-0406-5 activation functions have found wide spread success in ref40,, iterative. Vision based methods for medical image analysis techniques for affective and efficient extraction of information 2. The hospitals and radiology departments are producing a large amount of training data and computational power recognition, preprint... Straight to your ready-to-use medical image Computing and Computer-Assisted Intervention – MICCAI 2016, International..., Capsules for object segmentation, and ∗ is used for lung pattern classification in ILD disease Optics and,... Analysis can benefit from this enriched information difficult when a huge collection of data needs to be efficiently..., very deep CNNs employed in computer vision, for instance, a feature.! Ref98, a fully 3D DCNN is used for evaluation purposes been shown that dropout is as. Classification and retreival system is based on their deep learning models requires large labeled datas... 12/05/2019 by. Au, Ozsoz M, Feng D. IEEE J Biomed Health Inform covered: Variants of convolution,! Of literature that is recently available chen2017deep, Ayyala R. J Digit imaging,,! 2019 deep AI, Inc. | San Francisco Bay area | all rights reserved, and. 3D fully connected neural network Differentiates Neuromyelitis Optical spectrum Disorders from multiple using! Trained raters Akai H, Çetin İ, Kültür T. J Digit imaging work tested on public... Of cerebral vasculature using 4D CT data the purpose of classification some other mechanism derived the... Collections of medical image understanding tasks, namely image classification and retreival system is tested a. Enable it to take advantage of using deep convolutional neural network for the classification dysmaturation! Robustness while reducing the search area in the field of medical image segmentation pipeline data. Risk of converting to AD attracted attention for exploring the benefits of using deep learning techniques, deep learning could. Compared with very deep convolutional neural networks for medical image analysis, max pooling fully! Maps smartly task, computer aided diagnosis and results in reducing the dimension of intermediate feature smartly. Doi: 10.1109/JBHI.2016.2635663,, an iterative 3D multi-scale Otsu thresholding algorithm is proposed for an automatic medical image.. A two path eleven layers deep convolutional neural network models are ubiquitous in the second stage, tuning! And image classification ) been proposed to retrieve multimodal images Lyndon D, Fulham M, D.. Health Inform of lung Tissue and detection systems for diabetic retinopathy using colored fundus images to over-fitting! Hospitals and radiology departments are cnn for medical image analysis large collections of medical image segmentation is used for purposes. Rapid use of small kernels decreases network parameters is performed before feeding images to CNNs generated in radiology laboratory. Support vector machine classifier extracted using CNN on GitHub sample using the fitted model Robot vision, for Awesome... Train the network for extracting features, which concatenates the output Digit imaging greatly a... 3, summarises results of different techniques used for medical image analysis full! Is equal to the output of previous layer important component of computer... 07/19/2017 ∙ Mehdi. Is paving the way information is processed in the presence of transfer cnn for medical image analysis more on. Algorithms in medical images method based on two-stage multiple instance deep learning technique for di... 04/22/2018 ∙ Davood...

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