Deep Learning for Brain Tumor Segmentation
暂无分享,去创建一个
[1] William P. Marnane,et al. EEG compression using JPEG2000: How much loss is too much? , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.
[2] Jin Liu,et al. Applications of deep learning to MRI images: A survey , 2018, Big Data Min. Anal..
[3] Luis Perez,et al. The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.
[4] Muhammad Sharif,et al. Detection of Brain Tumor based on Features Fusion and Machine Learning , 2018, J. Ambient Intell. Humaniz. Comput..
[5] Atsushi Koike,et al. CT image compression with level of interest , 2004, 2004 International Conference on Image Processing, 2004. ICIP '04..
[6] A. Nait-Ali,et al. Comparison of wavelet based coders applied to 3D Brain Tumor MRI Images , 2009, 2009 6th International Multi-Conference on Systems, Signals and Devices.
[7] Brian B. Avants,et al. Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge , 2015, Medical Image Anal..
[8] U Rajendra Acharya,et al. Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques , 2019, Journal of Medical Systems.
[9] Tanzila Saba,et al. Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction , 2019, Journal of Medical Systems.
[10] Konstantinos N. Plataniotis,et al. Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries , 2018, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[11] J. Cinkler,et al. Lossless and near-lossless compression of EEG signals , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).
[12] Jamal Hussain Shah,et al. A novel machine learning approach for scene text extraction , 2018, Future Gener. Comput. Syst..
[13] V. Rajinikanth,et al. Entropy based segmentation of tumor from brain MR images - a study with teaching learning based optimization , 2017, Pattern Recognit. Lett..
[14] Muhammad Sharif,et al. A Survey on Medical Image Segmentation , 2015 .
[15] Roshan Joy Martis,et al. Recent advances in Big Data Analytics, Internet of Things and Machine Learning , 2018, Future Gener. Comput. Syst..
[16] Daniel G. Acevedo,et al. Robust Detection and Lossless Compression of the Foreground in Magnetic Resonance Images , 2010, 2010 Data Compression Conference.
[17] Nasir D. Memon,et al. Context-based lossless and near-lossless compression of EEG signals , 1999, IEEE Transactions on Information Technology in Biomedicine.
[18] Shivanand S. Gornale,et al. Multi-Resolution System for MRI (Magnetic Resonance Imaging) Image Compression: A Heterogeneous Wavelet Filters Bank Approach , 2007, Computer Graphics, Imaging and Visualisation (CGIV 2007).
[19] Zahid Iqbal,et al. Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection , 2018, Comput. Electron. Agric..
[20] Christopher Joseph Pal,et al. Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..
[21] Selin Aviyente,et al. Compressed Sensing Framework for EEG Compression , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.
[22] Walid Al-Atabany,et al. Multi-Classification of Brain Tumor Images Using Deep Neural Network , 2019, IEEE Access.
[23] V. Rajinikanth,et al. A reliable framework for accurate brain image examination and treatment planning based on early diagnosis support for clinicians , 2019, Neural Computing and Applications.
[24] Konstantinos Kamnitsas,et al. Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation , 2017, BrainLes@MICCAI.
[25] Yaozong Gao,et al. Fully convolutional networks for multi-modality isointense infant brain image segmentation , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).
[26] Alberto Signoroni,et al. Enabling solutions for an efficient compression of PET-CT datasets , 2009, 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC).
[27] Muhammad Sharif,et al. Brain Image Compression: A Brief Survey , 2013 .
[28] Brian B. Avants,et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) , 2015, IEEE Transactions on Medical Imaging.
[29] Muhammad Younus Javed,et al. An implementation of optimized framework for action classification using multilayers neural network on selected fused features , 2019, Pattern Analysis and Applications.
[30] Gang Li,et al. Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge , 2019, IEEE Transactions on Medical Imaging.
[31] Zhang Jian,et al. Application of Region Selective Embedded Zerotree Wavelet Coder in CT Image Compression , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.
[32] Konstantinos Kamnitsas,et al. Autofocus Layer for Semantic Segmentation , 2018, MICCAI.
[33] Dinggang Shen,et al. 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients , 2016, MICCAI.
[34] Muhammad Sharif,et al. Person re-identification with features-based clustering and deep features , 2019, Neural Computing and Applications.
[35] W. Yodchanan,et al. Lossless compression for 3-D MRI data using reversible KLT , 2008, 2008 International Conference on Audio, Language and Image Processing.
[36] Zhipeng Jia,et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features , 2017, BMC Bioinformatics.
[37] Klaus H. Maier-Hein,et al. Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge , 2017, BrainLes@MICCAI.
[38] Amir Alansary,et al. MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans , 2015, Comput. Intell. Neurosci..
[39] et al.,et al. Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge , 2018, ArXiv.
[40] Dimitriis A. Karras. Compression of MRI images using the discrete wavelet transform and improved parameter free Bayesian restoration techniques , 2009, 2009 IEEE International Workshop on Imaging Systems and Techniques.
[41] Wael Badawy,et al. MRI data compression using a 3-D discrete wavelet transform. , 2002, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.
[42] Victor Alves,et al. Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2016, IEEE Transactions on Medical Imaging.
[43] Konstantinos Kamnitsas,et al. Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..
[44] Chang Liu,et al. Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network , 2018, Journal of healthcare engineering.
[45] Tanzila Saba,et al. Brain tumor detection using statistical and machine learning method , 2019, Comput. Methods Programs Biomed..
[46] Yong Fan,et al. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation , 2017, Medical Image Anal..
[47] Sébastien Ourselin,et al. Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks , 2017, BrainLes@MICCAI.
[48] Michael L. Rhodes,et al. Locally Optimal Run-Length Compression Applied to CT Images , 1985, IEEE Transactions on Medical Imaging.
[49] Muhammad Sharif,et al. Multistage segmentation model and SVM-ensemble for precise lung nodule detection , 2018, International Journal of Computer Assisted Radiology and Surgery.
[50] Hervé Delingette,et al. 3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context , 2018, Comput. Medical Imaging Graph..
[51] Jing Yuan,et al. HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation , 2018, IEEE Transactions on Medical Imaging.
[52] Heesub Lee,et al. 3-D image compression for X-ray CT images using displacement estimation , 1991, [1991] Proceedings. Data Compression Conference.
[53] Christine Cavaro-Ménard,et al. Quality assessment of compressed cardiac MRI. Effect of lossy compression on computerized physiological parameters , 1999, Proceedings 10th International Conference on Image Analysis and Processing.
[54] Muhammad Sharif,et al. A framework for offline signature verification system: Best features selection approach , 2018, Pattern Recognit. Lett..
[55] Ju Lynn Ong,et al. False positive reduction in CT colonography using spectral compression and curvature tensor smoothing of surface geometry , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.
[56] Ehsan Ullah Munir,et al. Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection , 2018, Journal of Ambient Intelligence and Humanized Computing.