Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images Using a Convolutional Neural Network Scheme
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Taku Komura | Muhammad Febrian Rachmadi | Carlos Uziel Perez Malla | Maria del C. Valdes Hernandez | T. Komura | M. F. Rachmadi | M. F. Rachmadi | C. P. Leguízamo | M. Hernández
[1] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[2] Ronald M. Summers,et al. A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations , 2014, MICCAI.
[3] F. Barkhof,et al. Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)☆ , 2013, NeuroImage: Clinical.
[4] George Papandreou,et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation , 2018, ECCV.
[5] Isabelle Bloch,et al. From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning , 2017, 2017 IEEE International Conference on Image Processing (ICIP).
[6] Paul Babyn,et al. Generative Adversarial Network in Medical Imaging: A Review , 2018, Medical Image Anal..
[7] Garrett Fitzmaurice,et al. Statistical methods for assessing agreement. , 2002, Nutrition.
[8] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] J M Bland,et al. Statistical methods for assessing agreement between two methods of clinical measurement , 1986 .
[10] Kotagiri Ramamohanarao,et al. Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field , 2015, Comput. Medical Imaging Graph..
[11] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[12] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[13] Shin'ichi Satoh,et al. Learning More with Less: GAN-based Medical Image Augmentation , 2019, ArXiv.
[14] et al.,et al. ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI , 2017, Medical Image Anal..
[15] Min I. Chung. Neural network as generalized transversal filters , 1988, Neural Networks.
[16] J. R. Landis,et al. The measurement of observer agreement for categorical data. , 1977, Biometrics.
[17] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[18] Marleen de Bruijne,et al. Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols , 2015, IEEE Trans. Medical Imaging.
[19] Lisa Tang,et al. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation , 2016, IEEE Transactions on Medical Imaging.
[20] R. Bammer,et al. Real‐time diffusion‐perfusion mismatch analysis in acute stroke , 2010, Journal of magnetic resonance imaging : JMRI.
[21] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[22] E. Skinhøj,et al. Cerebral blood-flow. , 1972 .
[23] Y. Ni,et al. Magnetic resonance diffusion-perfusion mismatch in acute ischemic stroke: An update. , 2012, World journal of radiology.
[24] Sébastien Ourselin,et al. Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentations , 2017, DLMIA/ML-CDS@MICCAI.
[25] Y. Nesterov. A method for solving the convex programming problem with convergence rate O(1/k^2) , 1983 .
[26] D. Altman,et al. STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.
[27] D. Rueckert,et al. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks , 2017, NeuroImage: Clinical.
[28] Seyed-Ahmad Ahmadi,et al. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).
[29] E. Ringelstein,et al. Association between the Perfusion/Diffusion and Diffusion/FLAIR Mismatch: Data from the AXIS2 Trial , 2015, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[30] Nico Karssemeijer,et al. Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation , 2017, MICCAI.
[31] L. Mchenry. Cerebral blood flow. , 1966, The New England journal of medicine.
[32] Giovanni Montana,et al. Deep neural networks for anatomical brain segmentation , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[33] Taku Komura,et al. Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression using Irregularity Age Map in Brain MRI , 2018, bioRxiv.
[34] A. F. Adams,et al. The Survey , 2021, Dyslexia in Higher Education.
[35] Jeffrey L. Gunter,et al. Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks , 2018, SASHIMI@MICCAI.
[36] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[37] Taku Komura,et al. Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology , 2018, Comput. Medical Imaging Graph..
[38] Manuel Graña,et al. Brain White Matter Lesion Segmentation with 2D/3D CNN , 2017, IWINAC.
[39] Dazhe Zhao,et al. Ischemic Stroke Lesion Segmentation , 2015 .
[40] Arjan Durresi,et al. A survey: Control plane scalability issues and approaches in Software-Defined Networking (SDN) , 2017, Comput. Networks.
[41] J. Petrella,et al. MR perfusion imaging of the brain: techniques and applications. , 2000, AJR. American journal of roentgenology.
[42] Xiang Zhang,et al. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.
[43] Liang Chen,et al. GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks , 2018, ArXiv.
[44] Sergio Fantini,et al. Cerebral blood flow and autoregulation: current measurement techniques and prospects for noninvasive optical methods , 2016, Neurophotonics.
[45] Jian Sun,et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] Matthew D. Zeiler. ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.
[47] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[48] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[49] Andrew Zisserman,et al. Tabula rasa: Model transfer for object category detection , 2011, 2011 International Conference on Computer Vision.
[50] Davide Chicco,et al. Ten quick tips for machine learning in computational biology , 2017, BioData Mining.
[51] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.
[52] Nico Karssemeijer,et al. Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease. , 2016, Medical physics.
[53] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[54] Sébastien Ourselin,et al. An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation , 2017, MIUA.
[55] Shaohua Kevin Zhou,et al. Cross-Domain Synthesis of Medical Images Using Efficient Location-Sensitive Deep Network , 2015, MICCAI.
[56] A. Hillis,et al. Diffusion–Perfusion Mismatch: An Opportunity for Improvement in Cortical Function , 2015, Front. Neurol..
[57] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[58] Hayit Greenspan,et al. Synthetic data augmentation using GAN for improved liver lesion classification , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[59] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.