Voxelwise detection of cerebral microbleed in CADASIL patients by leaky rectified linear unit and early stopping

[1]  Ming Yang,et al.  Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization , 2018, Multimedia Tools and Applications.

[2]  Meikang Qiu,et al.  Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data , 2017, IEEE Systems Journal.

[3]  Huimin Lu,et al.  Facial Emotion Recognition Based on Biorthogonal Wavelet Entropy, Fuzzy Support Vector Machine, and Stratified Cross Validation , 2016, IEEE Access.

[4]  Yin Zhang,et al.  GroRec: A Group-Centric Intelligent Recommender System Integrating Social, Mobile and Big Data Technologies , 2016, IEEE Transactions on Services Computing.

[5]  Aboubakar Nasser Samatin Njikam,et al.  A novel activation function for multilayer feed-forward neural networks , 2016, Applied Intelligence.

[6]  H. Markus,et al.  CADASIL: Migraine, Encephalopathy, Stroke and Their Inter-Relationships , 2016, PloS one.

[7]  Yudong Zhang,et al.  Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection , 2016 .

[8]  Safdar Ali,et al.  Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data , 2016, Comput. Biol. Medicine.

[9]  A. Alexandrov,et al.  Risk of Symptomatic Intracerebral Hemorrhage After Intravenous Thrombolysis in Patients With Acute Ischemic Stroke and High Cerebral Microbleed Burden: A Meta-analysis. , 2016, JAMA neurology.

[10]  Yudong Zhang,et al.  Fruit classification by biogeography‐based optimization and feedforward neural network , 2016, Expert Syst. J. Knowl. Eng..

[11]  Osonde A. Osoba,et al.  The Noisy Expectation-Maximization Algorithm for Multiplicative Noise Injection , 2016 .

[12]  B. Montanini,et al.  Moonlighting transcriptional activation function of a fungal sulfur metabolism enzyme , 2016, Scientific Reports.

[13]  Kerenaftali Klein,et al.  A Bayesian Modelling Approach with Balancing Informative Prior for Analysing Imbalanced Data , 2016, PloS one.

[14]  Yudong Zhang,et al.  Artificial Intelligence and Its Applications 2014 , 2016 .

[15]  Latesh Malik,et al.  Employing Speeded Scaled Conjugate Gradient Algorithm for Multiple Contiguous Feature Vector Frames , 2016 .

[16]  S. Ando.  Classifying imbalanced data in distance-based feature space , 2016, Knowledge and Information Systems.

[17]  Bo Peng,et al.  Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection , 2016, Scientific Reports.

[18]  M. Moghaddam,et al.  Inverse Scattering Using a Joint $L1-L2$ Norm-Based Regularization , 2016, IEEE Transactions on Antennas and Propagation.

[19]  Dana S. Poole,et al.  The NOTCH3 score: a pre-clinical CADASIL biomarker in a novel human genomic NOTCH3 transgenic mouse model with early progressive vascular NOTCH3 accumulation , 2015, Acta neuropathologica communications.

[20]  Domenec Puig,et al.  Toward an optimal convolutional neural network for traffic sign recognition , 2015, International Conference on Machine Vision.

[21]  Olivier Salvado,et al.  Computer-aided detection of cerebral microbleeds in susceptibility-weighted imaging , 2015, Comput. Medical Imaging Graph..

[22]  Yudong Zhang,et al.  Magnetic Resonance Brain Image Classification via Stationary Wavelet Transform and Generalized Eigenvalue Proximal Support Vector Machine , 2015 .

[23]  Yang Li,et al.  Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks , 2015, Comput. Math. Methods Medicine.

[24]  Zohreh Azimifar,et al.  Fast Linear SVM Validation Based on Early Stopping in Iterative Learning , 2015, Int. J. Pattern Recognit. Artif. Intell..

[25]  Chrisina Jayne,et al.  Deep Dropout Artificial Neural Networks for Recognising Digits and Characters in Natural Images , 2015, ICONIP.

[26]  Arash Adib,et al.  Long-term streamflow forecasts by Adaptive Neuro-Fuzzy Inference System using satellite images and K-fold cross-validation (Case study: Dez, Iran) , 2015 .

[27]  D. Dziewulska,et al.  Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) - literature review apropos an autopsy case. , 2015, Polish journal of pathology : official journal of the Polish Society of Pathologists.

[28]  Daniel Imbeau,et al.  Adaptive neuro-fuzzy inference systems with k-fold cross-validation for energy expenditure predictions based on heart rate. , 2015, Applied ergonomics.

[29]  Yudong Zhang,et al.  Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization , 2015, Entropy.

[30]  Saeed Panahian Fard,et al.  Approximation of multivariate 2π-periodic functions by multiple 2π-periodic approximate identity neural networks based on the universal approximation theorems , 2015, 2015 11th International Conference on Natural Computation (ICNC).

[31]  Hayaru Shouno,et al.  Analysis of function of rectified linear unit used in deep learning , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[32]  Kohsuke Kudo,et al.  Susceptibility‐Weighted Phase Imaging and Oxygen Extraction Fraction Measurement during Sedation and Sedation Recovery using 7T MRI , 2015, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[33]  Yudong Zhang,et al.  Feed‐forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection , 2015, Int. J. Imaging Syst. Technol..

[34]  Limei Peng,et al.  CADRE: Cloud-Assisted Drug REcommendation Service for Online Pharmacies , 2014, Mobile Networks and Applications.

[35]  Wei Xu,et al.  Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier , 2015, IWBBIO.

[36]  Snehashis Roy,et al.  Cerebral microbleed segmentation from susceptibility weighted images , 2015, Medical Imaging.

[37]  Min Chen,et al.  AIWAC: affective interaction through wearable computing and cloud technology , 2015, IEEE Wireless Communications.

[38]  Bin Liao,et al.  An Image Retrieval Method for Binary Images Based on DBN and Softmax Classifier , 2015 .

[39]  Jee-Hyong Lee,et al.  An iterative undersampling of extremely imbalanced data using CSVM , 2015, Other Conferences.

[40]  Yudong Zhang,et al.  Fruit classification using computer vision and feedforward neural network , 2014 .

[41]  Haohua Zhao,et al.  Image Denoising with Rectified Linear Units , 2014, ICONIP.

[42]  P. Vitali,et al.  Cerebral Microbleed Causing an Acute Stroke-like Episode in a CADASIL Patient , 2014, Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques.

[43]  Reza Ghanbari,et al.  A modified scaled conjugate gradient method with global convergence for nonconvex functions , 2014 .

[44]  Victor C. M. Leung,et al.  CAP: community activity prediction based on big data analysis , 2014, IEEE Network.

[45]  Yudong Zhang,et al.  Artificial Intelligence and Its Applications , 2014 .

[46]  Yoojin Lee,et al.  A new susceptibility‐weighted image reconstruction method for the reduction of background phase artifacts , 2014, Magnetic resonance in medicine.

[47]  Rong Xuewen,et al.  Review and performance analysis of single hidden layer sequential learning algorithms of feed-forward neural networks , 2013, 2013 25th Chinese Control and Decision Conference (CCDC).

[48]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[49]  Susan M. Chang,et al.  Computer-aided detection of radiation-induced cerebral microbleeds on susceptibility-weighted MR images☆ , 2013, NeuroImage: Clinical.

[50]  Max A. Viergever,et al.  Efficient detection of cerebral microbleeds on 7.0T MR images using the radial symmetry transform , 2012, NeuroImage.

[51]  Yudong Zhang,et al.  A hybrid method for MRI brain image classification , 2011, Expert Syst. Appl..

[52]  E Mark Haacke,et al.  Semiautomated detection of cerebral microbleeds in magnetic resonance images. , 2011, Magnetic resonance imaging.

[53]  M. Seghier,et al.  Microbleed Detection Using Automated Segmentation (MIDAS): A New Method Applicable to Standard Clinical MR Images , 2011, PloS one.

[54]  Marios S. Pattichis,et al.  Multiscale AM-FM Demodulation and Image Reconstruction Methods With Improved Accuracy , 2010, IEEE Transactions on Image Processing.

[55]  Davar Khalili,et al.  Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions , 2010 .

[56]  D. Werring,et al.  The Microbleed Anatomical Rating Scale (MARS) , 2009, Neurology.

[57]  Xing-xing Wu,et al.  A New Early Stopping Algorithm for Improving Neural Network Generalization , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[58]  Steven Warach,et al.  Cerebral Microbleeds : A Field Guide to their Detection and Interpretation , 2012 .

[59]  E. Paul Zehr,et al.  A sigmoid function is the best fit for the ascending limb of the Hoffmann reflex recruitment curve , 2008, Experimental Brain Research.

[60]  T. Imaizumi,et al.  Dynamics of Dot‐Like Hemosiderin Spots on T2*‐Weighted MRIs Associated with Stroke Recurrence , 2007, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

[61]  Huimin Lu,et al.  Multiple Sclerosis Detection Based on Biorthogonal Wavelet Transform, RBF Kernel Principal Component Analysis, and Logistic Regression , 2016, IEEE Access.

[62]  Yudong Zhang,et al.  PATHOLOGICAL BRAIN DETECTION BY ARTIFICIAL INTELLIGENCE IN MAGNETIC RESONANCE IMAGING SCANNING (INVITED REVIEW) , 2016 .

[63]  Narayanan Kumarappan,et al.  Double Circuit EHV Transmission Lines Fault Location with RBF Based Support Vector Machine and Reconstructed Input Scaled Conjugate Gradient Based Neural Network , 2015, Int. J. Comput. Intell. Syst..

[64]  Domenec Puig,et al.  Recognizing Traffic Signs Using a Practical Deep Neural Network , 2015, ROBOT.

[65]  Yang Shao,et al.  Comparison of Early Stopping Criteria for Neural-Network-Based Subpixel Classification , 2011, IEEE Geoscience and Remote Sensing Letters.

[66]  C. Mares,et al.  Image Enhancement for Fingerprint Minutiae-Based Algorithms Using CLAHE, Standard Deviation Analysis and Sliding Neighborhood , 2008 .