Dilated Saliency U-Net for White Matter Hyperintensities Segmentation Using Irregularity Age Map
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Taku Komura | Muhammad Febrian Rachmadi | Yunhee Jeong | Maria del C. Valdes-Hernandez | T. Komura | M. F. Rachmadi | Maria del C. Valdés-Hernández | Yunhee Jeong
[1] Senthil Yogamani,et al. Analysis of Efficient CNN Design Techniques for Semantic Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[2] Daniel Rueckert,et al. Brain lesion segmentation through image synthesis and outlier detection , 2017, NeuroImage: Clinical.
[3] Keisuke Nemoto,et al. Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[4] Larry S. Davis,et al. AutoFocus: Efficient Multi-Scale Inference , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[5] L. R. Dice. Measures of the Amount of Ecologic Association Between Species , 1945 .
[6] C. Jack,et al. Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI) , 2005, Alzheimer's & Dementia.
[7] Mathieu Lamard,et al. Multiple-Instance Learning for Anomaly Detection in Digital Mammography , 2016, IEEE Transactions on Medical Imaging.
[8] Taku Komura,et al. Automatic Irregular Texture Detection in Brain MRI Without Human Supervision , 2018, MICCAI.
[9] D. Louis Collins,et al. Evaluating intensity normalization on MRIs of human brain with multiple sclerosis , 2011, Medical Image Anal..
[10] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Daniel Rueckert,et al. Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images , 2019, Comput. Medical Imaging Graph..
[12] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[13] Thomas A. Funkhouser,et al. Dilated Residual Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Naftali Raz,et al. Volume of white matter hyperintensities in healthy adults: contribution of age, vascular risk factors, and inflammation-related genetic variants. , 2012, Biochimica et biophysica acta.
[15] Tanveer F. Syeda-Mahmood,et al. Saliency U-Net: A regional saliency map-driven hybrid deep learning network for anomaly segmentation , 2018, Medical Imaging.
[16] 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.
[17] P. Mayorga,et al. Automatic brain tumor detection and segmentation in MR images , 2014, 2014 Pan American Health Care Exchanges (PAHCE).
[18] William M. Wells,et al. Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.
[19] Raquel Urtasun,et al. Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds , 2018, 2018 International Conference on 3D Vision (3DV).
[20] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[21] Thomas Brox,et al. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.
[22] Mitko Veta,et al. Adversarial Training and Dilated Convolutions for Brain MRI Segmentation , 2017, DLMIA/ML-CDS@MICCAI.
[23] J. Wardlaw,et al. Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke , 2015, Brain and behavior.
[24] H. Möller,et al. Regional Distribution of White Matter Hyperintensities in Vascular Dementia, Alzheimer’s Disease and Healthy Aging , 2004, Dementia and Geriatric Cognitive Disorders.
[25] Guang Yang,et al. Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks , 2017, MIUA.
[26] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[27] Byoung-Tak Zhang,et al. Multi-focus Attention Network for Efficient Deep Reinforcement Learning , 2017, AAAI Workshops.
[28] Johan H. C. Reiber,et al. Fully automatic segmentation of white matter hyperintensities in MR images of the elderly , 2005, NeuroImage.
[29] Muhammad Febrian Rachmadi,et al. Voxel-based irregularity age map (IAM) for brain's white matter hyperintensities in MRI , 2017, 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS).
[30] Yue Zhang,et al. A post-processing method to improve the white matter hyperintensity segmentation accuracy for randomly-initialized U-net , 2018, 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP).
[31] Jonathan Ventura,et al. Dilated Convolutions for Brain Tumor Segmentation in MRI Scans , 2017, BrainLes@MICCAI.
[32] Georg Langs,et al. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.
[33] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[34] Asmita Ray,et al. Brain tumor detection and segmentation , 2016 .
[35] D. Rueckert,et al. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks , 2017, NeuroImage: Clinical.
[36] Norbert Schuff,et al. Neuropathological basis of magnetic resonance images in aging and dementia , 2008, Annals of neurology.
[37] Jeffrey N. Chiang,et al. Optimized Brain Extraction for Pathological Brains (optiBET) , 2014, PloS one.
[38] Daniel Cohen-Or,et al. Time-varying weathering in texture space , 2016, ACM Trans. Graph..
[39] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[40] P. Scheltens,et al. White matter lesions on magnetic resonance imaging in clinically diagnosed Alzheimer's disease. Evidence for heterogeneity. , 1992, Brain : a journal of neurology.