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Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding

Yawen Huang, Ling Shao, Alejandro F. Frangi
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We propose the weakly-supervised joint convolutional sparse coding to simultaneously solve the problems of super-resolution (SR) and cross-modality image synthesis.  ...  Local image neighborhoods are naturally preserved by operating on the whole image domain (as opposed to image patches) and using joint convolutional sparse coding.  ...  Brain MRI Super-Resolution For the problem of image SR, we focus on the PD-w subjects of the IXI dataset to compare the proposed WEENIE model with several state-of-the-art SR approaches: sparse coding-based  ... 
doi:10.1109/cvpr.2017.613 dblp:conf/cvpr/HuangSF17 fatcat:cqvxi334xvgopjoc6gbttph5ve

Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images using Weakly-Supervised Joint Convolutional Sparse Coding [article]

Yawen Huang, Ling Shao, Alejandro F. Frangi
2017 arXiv   pre-print
We propose the weakly-supervised joint convolutional sparse coding to simultaneously solve the problems of super-resolution (SR) and cross-modality image synthesis.  ...  Local image neighborhoods are naturally preserved by operating on the whole image domain (as opposed to image patches) and using joint convolutional sparse coding.  ...  Brain MRI Super-Resolution For the problem of image SR, we focus on the PD-w subjects of the IXI dataset to compare the proposed WEENIE model with several state-of-the-art SR approaches: sparse coding-based  ... 
arXiv:1705.02596v1 fatcat:wiupz3otjrc7tgurg37xprea2y

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Art Multimodal Target Detection by Sparse Coding: Application to Paint Loss Detection in Paintings.  ...  Chen, Y., +, TIP 2020 3596-3611 Data acquisition Learning to Reconstruct and Understand Indoor Scenes From Sparse Views.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Dynamic network coding of working-memory domains and working-memory processes

Eyal Soreq, Robert Leech, Adam Hampshire
2019 Nature Communications  
Here, we use machine-learning to determine how aspects of WM are dynamically coded in the human brain.  ...  Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively to brain areas or processing streams; however, the mappings from that literature form salient features within  ...  All custom MATLAB routines and data used to generate the analysis and figures of this paper will be committed upon acceptance for review to the following GitHub: https://github.com/esoreq/WM.  ... 
doi:10.1038/s41467-019-08840-8 pmid:30804436 pmcid:PMC6389921 fatcat:ucbhuevfrbfybhazluva43ndz4

Learning Robust Data Representation: A Knowledge Flow Perspective [article]

Zhengming Ding and Ming Shao and Handong Zhao and Sheng Li
2020 arXiv   pre-print
It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompleteness or knowledge domain  ...  This would benefit AI community from literature review to future direction.  ...  The very pioneering work is robust sparse transfer coding, whose idea is to seek domain-invariant sparse coding for both domains and the objective function can be formulated as X−DZ 2 F + γ Z 1 + αR(Z)  ... 
arXiv:1909.13123v2 fatcat:wll23rkrznejvhzsihc6rwcwve

Neural Representations for Object Perception: Structure, Category, and Adaptive Coding

Zoe Kourtzi, Charles E. Connor
2011 Annual Review of Neuroscience  
may optimize the object code.  ...  Object perception is one of the most remarkable capacities of the primate brain.  ...  Successful cross-prediction of responses between runs (Figure 2e ) demonstrates that the adaptive search algorithm converged on the same result from different starting points.  ... 
doi:10.1146/annurev-neuro-060909-153218 pmid:21438683 fatcat:j6nwnuwy75d4nfnhvvtkky4x3q

GRACE: A Visual Comparison Framework for Integrated Spatial and Non-Spatial Geriatric Data

Adrian Maries, Nathan Mays, Megan Olson Hunt, Kim F. Wong, William Layton, Robert Boudreau, Caterina Rosano, G. Elisabeta Marai
2013 IEEE Transactions on Visualization and Computer Graphics  
The visual analysis framework blends medical imaging, mathematical analysis and interactive visualization techniques, and includes the adaptation of Sparse Partial Least Squares and iterated Tikhonov Regularization  ...  In addition to the domain analysis and design description, we demonstrate the usefulness of this approach on two case studies.  ...  Thanks to Sriranjani Mandayam and John Yackovich for an early volume rendering prototype, and to the Pitt Vislab for feedback.  ... 
doi:10.1109/tvcg.2013.161 pmid:24051859 pmcid:PMC4423600 fatcat:mtnriqquurcwhpiqixnhdcxzxy

Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World [article]

Sahil Garg, Irina Rish, Guillermo Cecchi, Aurelie Lozano
2017 arXiv   pre-print
in the dentate gyrus of the hippocampus, known to be associated with improved cognitive function and adaptation to new environments.  ...  (nonadaptive) online sparse coding of Mairal et al. (2009) in the presence of nonstationary data.  ...  As it turns out, the fixed-size ODL approach has difficulties adapting to a new domain in nonstationary settings, when the data in both domains are sparse and, across the domains, the supports (i.e., the  ... 
arXiv:1701.06106v2 fatcat:ulcgefsabrggzedszxyng532fu

Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation [article]

Shuo Zhou, Christopher R Cox, Haiping Lu
2018 biorxiv/medrxiv   pre-print
In this paper, we proposed a domain adaptation framework for whole-brain fMRI (DawfMRI) to improve whole-brain neural decoding on target data leveraging pre-existing source data.  ...  The results demonstrated that appropriate source domain can help improve neural decoding accuracy for challenging classification tasks. The best-case improvement is 8.94% (from 78.64% to 87.58%).  ...  Acknowledgment This work was supported by grants from the UK Engineering and Physi-  ... 
doi:10.1101/375030 fatcat:c4cbany7bfe4bnzgtbpgvgo4ca

Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity

Yinghua Li, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani
2019 IEEE Access  
INDEX TERMS Brain MRI, super-resolution, dictionary, sparse representation, compressed sensing, self-similarity.  ...  We use the linear relationship among images in the measurement domain and frequency domain to classify image blocks into smooth, texture, and edge feature blocks in the measurement domain.  ...  FIGURE 5 . 5 Comparison of visual results with upscaling factor 2 (brain MRI image from Brainweb). FIGURE 6 . 6 Comparison of visual results with upscaling factor 2 (brain MRI image from MRT).  ... 
doi:10.1109/access.2019.2900125 fatcat:4phbyarrzvf4li4a4fomlpehlq

2020 Index IEEE Signal Processing Letters Vol. 27

2020 IEEE Signal Processing Letters  
Restoration of Lossy JPEG-Compressed Brain MR Images Using Cross-Domain Neural Networks.  ...  ., +, LSP 2020 2009-2013 Restoration of Lossy JPEG-Compressed Brain MR Images Using Cross-Domain Neural Networks.  ... 
doi:10.1109/lsp.2021.3055468 fatcat:wfdtkv6fmngihjdqultujzv4by

Brain Image Synthesis with Unsupervised Multivariate Canonical CSCℓ_4Net [article]

Yawen Huang, Feng Zheng, Danyang Wang, Weilin Huang, Matthew R. Scott, Ling Shao
2021 arXiv   pre-print
There is a clear need to go beyond the traditional imaging-dependent process and synthesize anatomically specific target-modality data from a source input.  ...  In this paper, we propose to learn dedicated features that cross both intre- and intra-modal variations using a novel CSCℓ_4Net.  ...  Motivated by the state-of-the-art domain adaptation works [21, 5] , we cross-transfer both intra-modal and inter-modal features to a high-level projective space to handle the multivariate heterogeneous  ... 
arXiv:2103.11587v1 fatcat:its7pliaa5g77efer5s3zjhyyu

Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection [article]

Liang Du and Xiaoqing Ye and Xiao Tan and Jianfeng Feng and Zhenbo Xu and Errui Ding and Shilei Wen
2020 arXiv   pre-print
This domain adaptation approach mimics the functionality of the human brain when proceeding object perception.  ...  In this paper, we innovatively propose a domain adaptation like approach to enhance the robustness of the feature representation.  ...  (c) The perceptual-to-conceptual module (P2C) adapts features from the perceptual domain to the conceptual domain, which mimics the knowledge association and retrieval process in the brain.  ... 
arXiv:2006.04356v1 fatcat:f6e4wjx365hyndi4aztnmqqzfi

Table of contents

2012 IEEE Transactions on Image Processing  
Tao 284 An Algorithm for the Contextual Adaption of SURF Octave Selection With Good Matching Performance: Best Octaves . ............. ..................................................................  ...  Xu (Contents Continued on Back Cover) (Contents Continued from Front Cover) Lossy Coding of Images and Video Mosaicing Edge Strength Filter Based Color Filter Array Interpolation . . . . . . .  ... 
doi:10.1109/tip.2011.2178726 fatcat:vkwtzivdmjc27ljutlfppeb66y

A universal model of esthetic perception based on the sensory coding of natural stimuli

Christoph Redies
2008 Spatial Vision  
Moreover, evidence from neurophysiological experiments shows that the visual system uses an efficient (sparse) code to process optimally the statistical properties of natural stimuli.  ...  This resonant state is thought to be based on the adaptation of the visual system to natural scenes.  ...  The author wishes to thank Lothar Spillmann and Gregor Paul for critical reading of the manuscript and anonymous reviewers for constructive criticism.  ... 
doi:10.1163/156856808782713780 fatcat:7aqv4eglfbfxllgdsjdwoxnnye
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