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A microstructure estimation Transformer inspired by sparse representation for diffusion MRI

MEDICAL IMAGE ANALYSIS 2023年 86卷 102788页

作者： Zheng, Tianshu Yan, Guohui Li, Haotian Zheng, Weihao Shi, Wen Zhang, Yi Ye, Chuyang Wu, Dan Zhejiang Univ Coll Biomed Engn & Instrument Sci Dept Biomed Engn Hangzhou Zhejiang Peoples R China Zhejiang Univ Womens Hosp Dept Radiol Sch Med Hangzhou Zhejiang Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing Peoples R China

Diffusion magnetic resonance imaging (dMRI) is an important tool in characterizing tissue microstructure based on biophysical models, which are typically multi-compartmental models with mathematically complex and highly non-linear forms. Resolving microstructures from these models with conventional optimization techniques is prone to estimation errors and requires dense sampling in the q-space with a long scan time. Deep learning based approaches have been proposed to overcome these limitations. Motivated by the superior performance of the Transformer in feature extraction than the convolutional structure, in this work, we present a learning-based framework based on Transformer, namely, a Microstructure Estimation Transformer with sparse coding (METSC) for dMRI-based microstructural parameter estimation. To take advantage of the Transformer while addressing its limitation in large training data requirement, we explicitly introduce an inductive bias- model bias into the Transformer using a sparse coding technique to facilitate the training process. Thus, the METSC is composed with three stages, an embedding stage, a sparse representation stage, and a mapping stage. The embedding stage is a Transformer-based structure that encodes the signal in a high-level space to ensure the core voxel of a patch is represented effectively. In the sparse representation stage, a dictionary is constructed by solving a sparse reconstruction problem that unfolds the Iterative Hard Thresholding (IHT) process. The mapping stage is essentially a decoder that computes the microstructural parameters from the output of the second stage, based on the weighted sum of normalized dictionary coefficients where the weights are also learned. We tested our framework on two dMRI models with downsampled q-space data, including the intravoxel incoherent motion (IVIM) model and the neurite orientation dispersion and density imaging (NODDI) model. The proposed method achieved up to 11.25 folds of accelera

关键词： Diffusion MRI Microstructural model sparse coding Transformer

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Transfer of sparse coding Representations And Object Classifiers Across Heterogeneous Robots

Transfer of Sparse Coding Representations And Object Classif...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Zsolt Kira Georgia Tech Research Institute Atlanta GA 30318 USA

ISBN: (纸本)9781479969357

This paper examines the problem of transfer learning in the context of object recognition in a heterogeneous robot team. We specifically look at the case where robots individually learn object classifiers and must then transfer the resulting learned knowledge to another robot. Recent trends in computer vision and robotics have moved towards feature representation learning, where the underlying feature representation used in classification is learned in a data-driven way. This poses a problem to knowledge transfer, as the underlying representations learned by different robots will differ significantly. In this paper, we present several hypotheses with regard to knowledge transfer in such a scenario, specifically that 1) the transfer of knowledge will be most effective if it involves not just the classifier itself, but the learned feature representations themselves, 2) this is not a problem because given similar scenes and objects, some methods such as sparse coding are able to learn representations that can be successfully used by another robot, and 3) a codebook encoding scheme such as Fisher vectors will result in a smaller reduction in accuracy after transfer even if the receiving robot uses its own learned feature representation. Finally, we contribute an alignment procedure and demonstrate that it can serve to facilitate knowledge transfer even when the underlying feature representations are independently learned by each robot and codebook methods are not used. We test all three of the hypotheses and the alignment procedure on a real-world dataset consisting of two robots viewing the same 12 objects using cameras with differing characteristics.

关键词： alignment procedure Transfer (Psychology) Robot sparse coding object recognition representations Classifiers heterogeneous robot

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BRAIN TUMOR CLASSIFICATION USING sparse coding AND DICTIONARY LEARNING

BRAIN TUMOR CLASSIFICATION USING SPARSE CODING AND DICTIONAR...

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IEEE International Conference on Image Processing

作者： Saif Dawood Salman Al-Shaikhli Michael Ying Yang Bodo Rosenhahn Institute for Information Processing Leibniz University Hannover

ISBN: (纸本)9781479957521

Brain tumor classification is considered as one of the most challenging tasks in medical imaging. In this paper, a novel approach for multi-class brain tumor classification based on sparse coding and dictionary learning is proposed. We propose an individual (per-class) dictionary learning and sparse coding classification using K-SVD algorithm. This approach combines topological and texture features to build and learn a dictionary. Experimental results demonstrate that the sparse coding based classification outperforms other state-of-the-art methods.

关键词： Brain tumor classification Dictionary learning sparse coding Topological matrix Gray level co-occurrence matrix

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A novel multi-branch wavelet neural network for sparse representation based object classification

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PATTERN RECOGNITION 2023年 135卷

作者： Nguyen, Tan-Sy Luong, Marie Kaaniche, Mounir Ngo, Long H. Beghdadi, Azeddine Univ Sorbonne Paris Nord Lab Anal Geometrie & Applicat LAGA CNRS UMPR 7539 Villetaneuse France Univ Sorbonne Paris Nord Lab Traitement & Transport Informat L2TI UR 3043 F-93430 Villetaneuse France

Recent advances in acquisition and display technologies have led to an enormous amount of visual data, which requires appropriate storage and management tools. One of the fundamental needs is the design of efficient image classification and recognition solutions. In this paper, we propose a wavelet neural network approach for sparse representation-based object classification. The proposed approach aims to exploit the advantages of sparse coding, multi-scale wavelet representation as well as neural networks. More precisely, a wavelet transform is firstly applied to the image datasets. The generated approximation and detail wavelet subbands are then fed into a multi-branch neural network architecture. This architecture produces multiple sparse codes that are efficiently combined during the classification stage. Extensive experiments, carried out on various types of standard object datasets, have shown the efficiency of the proposed method compared to the existing sparse coding and deep learning-based methods.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Object classification sparse coding Wavelet transform Neural networks Multi-branch architecture

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Constrained sparse Concept coding algorithm with application to image representation

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2014年第9期8卷 3211-3230页

作者： Shu, Zhenqiu Zhao, Chunxia Huang, Pu Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

Recently, sparse coding has achieved remarkable success in image representation tasks. In practice, the performance of clustering can be significantly improved if limited label information is incorporated into sparse coding. To this end, in this paper, a novel semi-supervised algorithm, called constrained sparse concept coding (CSCC), is proposed for image representation. CSCC considers limited label information into graph embedding as additional hard constraints, and hence obtains embedding results that are consistent with label information and manifold structure information of the original data. Therefore, CSCC can provide a sparse representation which explicitly utilizes the prior knowledge of the data to improve the discriminative power in clustering. Besides, a kernelized version of our proposed CSCC, namely kernel constrained sparse concept coding (KCSCC), is developed to deal with nonlinear data, which leads to more effective clustering performance. The experimental evaluations on the MNIST, PIE and Yale image sets show the effectiveness of our proposed algorithms.

关键词： sparse coding label information semi-supervised constraints manifold kernelized

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Neural decoding reveals specialized kinematic tuning after an abrupt cortical transition

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CELL REPORTS 2023年第9期42卷 113119页

作者： Glanz, Ryan M. Sokoloff, Greta Blumberg, Mark S. Univ Iowa Dept Psychol & Brain Sci Iowa City IA 52242 USA Univ Iowa Iowa Neurosci Inst Iowa City IA 52242 USA

The primary motor cortex (M1) exhibits a protracted period of development, including the development of a sensory representation long before motor outflow emerges. In rats, this representation is present by post-natal day (P) 8, when M1 activity is "discontinuous."Here, we ask how the representation changes upon the transition to "continuous"activity at P12. We use neural decoding to predict forelimb movements from M1 activity and show that a linear decoder effectively predicts limb movements at P8 but not at P12;instead, a nonlinear decoder better predicts limb movements at P12. The altered decoder performance reflects increased complexity and uniqueness of kinematic information in M1. We next show that M1's representation at P12 is more susceptible to "lesioning"of inputs and "transplanting"of M1's encoding scheme from one pup to another. Thus, the emergence of continuous M1 activity signals the developmental onset of more complex, informationally sparse, and individualized sensory representations.

关键词： development rat REM sleep twitching motor cortex neural decoding sensorimotor sparse coding LSTM mutual information Research topic(s)CP: Neuroscience

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LDA BASED COMPACT AND DISCRIMINATIVE DICTIONARY LEARNING FOR sparse coding

LDA BASED COMPACT AND DISCRIMINATIVE DICTIONARY LEARNING FOR...

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IEEE International Conference on Image Processing

作者： Jiayong Chen Xiangmin Xu Chunmei Qing Jianxiu Jin School of Electronic and Information Engineering South China University of Technology

ISBN: (纸本)9781479957521

The dictionary response usually affects the recognition results directly as it represents the original data and usually serves as the input of the classifier. However, the over-complete dictionary usually results in high dimensional response and redundancy. The application of the linear discriminant analysis (LDA)-based mapping method transforms the original dictionary response to be more discriminative for compact dictionary learning, resulting in high intra-class similarity and high inter-class dissimilarity in the response domain for better classification. By analyzing the recognition rate, the compactness and the purity, the proposed method can learn a small size of compact and discriminative dictionary with global optimization, and it can get a comparable or even better performance than the over-complete dictionary with much less computation cost. Experimental results demonstrate that the proposed approach also outperforms several recently proposed compact dictionary learning methods on human action recognition and object classification.

关键词： sparse coding LDA Compact dictionary learning Action recognition Object classification

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Comparative Analysis of Digital Elevation Model Generation Methods Based on sparse Modeling

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REMOTE SENSING 2023年第11期15卷 2714-2714页

作者： Fuse, Takashi Imose, Kazuki Univ Tokyo Dept Civil Engn 7-3-1 HongoBunkyo Ku Tokyo 1138656 Japan Cent Japan Railway Co JR Tokai Shinagawa Bldg A2-1-85 KonanMinato Ku Tokyo 1088204 Japan

With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce digital elevation models (DEMs) with sufficient accuracy. If the topographic features are considered as a basis, the DEM should be reproducible based on a combination of such features. The purpose of this study is to develop new DEM generation methods based on sparse modeling. Based on a review of the definitions of sparsity, we developed DEM generation methods based on a discrete cosine transform (DCT), DCT with elastic net, K-singular value decomposition (K-SVD), Fourier regularization, wavelet regularization, and total variation (TV) minimization, and conducted a comparative analysis. The developed methods were applied to artificially deficient DEM and ALB data, and their accuracy was evaluated. Thus, as a conclusion, we can confirm that the K-SVD method is appropriate when the percentage of deficiencies is low, and that the TV minimization method is appropriate when the percentage of deficiencies is high. Based on these results, we also developed a method integrating both methods and achieved an RMSE of 0.128 m.

关键词： digital elevation model (DEM) generation aerial laser bathymetry (ALB) sparse modeling sparse coding compressed sensing total variation comparative analysis

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MOBILE VISUAL SEARCH VIA HIEVARCHICAL sparse coding

MOBILE VISUAL SEARCH VIA HIEVARCHICAL SPARSE CODING

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IEEE International Conference on Multimedia and Expo

作者： Xiyu Yang Lianli Liu Xueming Qian Tao Mei Jialie Shen Qi Tian SMILES LAB Xi'an Jiaotong University Microsoft Research Signapore Management University University of Texas at San Antonia

ISBN: (纸本)9781479947607

Mobile visual search is attracting much research attention recently. Existing works focus on addressing the limited capacity of wireless channel yet overlook its instability, thus is not adaptive to the change of channel capacity. In this paper, a novel image retrieval algorithm that is scalable to various channel condition is proposed. The proposed algorithm contains three contributions: (1) to achieve instant retrieval under various channel capacity, we adjust transmission load by sparseness instead of codebook size;(2) we introduce hierarchical sparse coding into our retrieval workflow, where original codebook is transformed into a tree-structured dictionary which implies elements' priority;(3) we propose transmission priority ranking schemes that is adaptive to specific query. Experiment results show that the proposed algorithm outperforms BoW and Lasso based algorithm under different parameter settings. Retrieval results under different channel limitation validate the scalability of our method.

关键词： Image retrieval Mobile Hierarchical sparse coding Visual search

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MINIMIZING DATASET BIAS: DISCRIMINATIVE MULTI-TASK sparse coding THROUGH SHARED SUBSPACE LEARNING FOR IMAGE CLASSIFICATION

MINIMIZING DATASET BIAS: DISCRIMINATIVE MULTI-TASK SPARSE CO...

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IEEE International Conference on Image Processing

作者： Gaowen Liu Yan Yan Jingkuan Song Nicu Sebe Dept. of Inf. Eng. & Comput. Sci. Univ. of Trento Trento Italy

ISBN: (纸本)9781479957521

sparse coding was shown to be able to find succinct representations of stimuli. Recently, it has been successfully applied to a variety of problems in image processing analysis. sparse coding models data vectors as a linear combination of a few elements from a dictionary. However, most existing sparse coding methods are applied for a single task on a single dataset. The learned dictionary is then possibly biased towards the specific dataset and lacks of generalization abilities. In light of this, in this paper we propose a multitask sparse coding approach by uncovering a shared subspace among heterogeneous datasets. The proposed multi-task coding strategy leverages the commonality benefit from different datasets. Moreover, our multi-task coding framework is capable of direct classification by incorporating label information. Experimental results show that the dictionary learned by our approach has more generalization abilities and our model performs better classification compared to the model learned from only one dataset or the model learned from simply pooling different datasets together.

关键词： image classification image coding learning (artificial intelligence) discriminative multi-task sparse coding heterogeneous datasets image processing analysis shared subspace learning sparse coding method Accuracy Algorithm design and analysis Dictionaries Encoding Feature extraction Image coding Training Dataset Bias Multi-task Shared Subspace sparse coding sparse coding Image classification image coding Dictionaries as Topic Algorithm design and analysis Dataset Feature extraction process analysis Functional training

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