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Split-KalmanNet: A Robust model-based deep learning Approach for State Estimation

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第9期72卷 12326-12331页

作者： Choi, Geon Park, Jeonghun Shlezinger, Nir Eldar, Yonina C. Lee, Namyoon POSTECH Dept Elect Engn Pohang 37673 South Korea Yonsei Univ Sch Elect & Elect Engn Seoul 03722 South Korea Ben Gurion Univ Negev Sch Elect & Comp Engn IL-84105 Beer Sheva Israel Weizmann Inst Sci Math & CS Fac IL-7610001 Rehovot Israel Korea Univ Sch Elect Engn Seoul 02841 South Korea

Estimation of the state of a discrete-time state-space model from noisy measurements is a crucial aspect of signal processing. The extended Kalman filter (EKF) is widely used as a low-complexity solution based on a state evolution and measurement model of the state-space model. However, obtaining precise information about these models can be difficult in practice, and model mismatch greatly reduces state estimation accuracy. In this paper, we introduce Split-KalmanNet, a robust EKF algorithm that utilizes the power of deep learning for state estimation. Split-KalmanNet is inspired by the recent KalmanNet and calculates the Kalman gain using the Jacobian matrix of a measurement function and two recurrent neural networks (RNNs) with a split structure. The RNNs independently learn the covariance matrices of the prior state estimate and the innovation from data. The proposed split structure in calculating the Kalman gain allows for compensation of both state and measurement model mismatch effects. Numerical simulations show that Split-KalmanNet outperforms traditional EKF and the state-of-the-art KalmanNet algorithm in various scenarios of model mismatch.

关键词： Kalman filter model-based deep learning state-space model sequential state estimation

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Memory-Efficient model-based deep learning With Convergence and Robustness Guarantees

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING

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IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2023年 9卷 260-275页

作者： Pramanik, Aniket Zimmerman, M. Bridget Jacob, Mathews Univ Iowa Dept Elect & Comp Engn Iowa City IA 52242 USA Univ Iowa Dept Biostat Iowa City IA 52242 USA

Computational imaging has been revolutionized by compressed sensing algorithms, which offer guaranteed uniqueness, convergence, and stability properties. model-based deep learning methods that combine imaging physics with learned regularization priors have emerged as more powerful alternatives for image recovery. The main focus of this paper is to introduce a memory efficient model-based algorithm with similar theoretical guarantees as CS methods. The proposed iterative algorithm alternates between a gradient descent involving the score function and a conjugate gradient algorithm to encourage data consistency. The score function is modeled as a monotone convolutional neural network. Our analysis shows that the monotone constraint is necessary and sufficient to enforce the uniqueness of the fixed point in arbitrary inverse problems. In addition, it also guarantees the convergence to a fixed point, which is robust to input perturbations. We introduce two implementations of the proposed MOL framework, which differ in the way the monotone property is imposed. The first approach enforces a strict monotone constraint, while the second one relies on an approximation. The guarantees are not valid for the second approach in the strict sense. However, our empirical studies show that the convergence and robustness of both approaches are comparable, while the less constrained approximate implementation offers better performance. The proposed deep equilibrium formulation is significantly more memory efficient than unrolled methods, which allows us to apply it to 3D or 2D+time problems that current unrolled algorithms cannot handle.

关键词： Convergence Imaging Robustness Perturbation methods Computational complexity Training Three-dimensional displays deep equilibrium models model-based deep learning monotone operator learning

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Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping

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MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2024年第3期37卷 411-427页

作者： Venkatesh, Vaddadi Mathew, Raji Susan Yalavarthy, Phaneendra K. Indian Inst Sci Dept Computat & Data Sci Bangalore 560012 Karnataka India Indian Inst Sci Educ & Res Sch Data Sci Thiruvananthapuram 695551 Kerala India

Objective Quantitative susceptibility mapping (QSM) provides an estimate of the magnetic susceptibility of tissue using magnetic resonance (MR) phase measurements. The tissue magnetic susceptibility (source) from the measured magnetic field distribution/local tissue field (effect) inherent in the MR phase images is estimated by numerically solving the inverse source-effect problem. This study aims to develop an effective model-based deep-learning framework to solve the inverse problem of QSM. Materials and methods This work proposes a Schatten p-norm-driven model-based deep learning framework for QSM with a learnable norm parameter p to adapt to the data. In contrast to other model-based architectures that enforce the l2-norm or l1-norm for the denoiser, the proposed approach can enforce any p-norm (0

deep learning-based approaches, such as QSMnet, and model-based deep learning approaches, such as learned proximal convolutional neural network (LPCNN). Reconstructions performed using 77 imaging volumes with different acquisition protocols and clinical conditions, such as hemorrhage and multiple sclerosis, showed that the proposed approach outperformed existing state-of-the-art methods by a significant margin in terms of quantitative merits. Conclusion The proposed SpiNet-QSM showed a consistent improvement of at least 5% in terms of the high-frequency error norm (HFEN) and normalized root mean squared error (NRMSE) over other QSM reconstruction methods with limited training data.

关键词： Dipole inversion model-based deep learning Schatten p-norm Susceptibility reconstruction

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Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A model-based deep learning Approach

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IEEE COMMUNICATIONS LETTERS 2023年第4期27卷 1155-1159页

作者： Zhang, Xiangyu Wang, Zening Zhang, Haiyang Yang, Luxi Southeast Univ Sch Informat Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China Purple Mt Labs Pervas Commun Ctr Nanjing 210023 Peoples R China AsiaInfo Technol Ltd Beijing 100193 Peoples R China Nanjing Univ Posts & Telecommun Sch Commun & Informat Engn Nanjing 210049 Peoples R China

Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising technology for future wireless communications. The deployment of XL-MIMO, especially at high-frequency bands, leads to users being located in the near-field region instead of the conventional far-field. This letter proposes efficient model-based deep learning algorithms for estimating the near-field wireless channel of XL-MIMO communications. In particular, we first formulate the XL-MIMO near-field channel estimation task as a compressed sensing problem using the spatial gridding-based sparsifying dictionary and then solve the resulting problem by applying the learning Iterative Shrinkage and Thresholding Algorithm (LISTA). Due to the near-field characteristic, the spatial gridding-based sparsifying dictionary may result in low channel estimation accuracy and a heavy computational burden. To address this issue, we propose a novel sparsifying dictionary learning-LISTA (SDL-LISTA) algorithm that formulates the sparsifying dictionary as a neural network layer and embeds it into LISTA neural network. The numerical results show that our proposed algorithms outperform non-learning benchmark schemes, and SDL-LISTA achieves better performance than LISTA with ten times atoms reduction.

关键词： Channel estimation Dictionaries Radio frequency deep learning Wireless communication Matching pursuit algorithms Neural networks Near-field channel estimation extremely large MIMO model-based deep learning

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LMPDNET: TOF-PET LIST-MODE IMAGE RECONSTRUCTION USING model-based deep learning METHOD 30

LMPDNET: TOF-PET LIST-MODE IMAGE RECONSTRUCTION USING MODEL-...

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30th IEEE International Conference on Image Processing (ICIP)

作者： Li, Chenxu Fang, Jingwan Hu, Rui Cui, Jianan Liu, Huafeng Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728198354

The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) improves image qualities. However, implementing the cutting-edge model-based deep learning methods for TOF-PET reconstruction is challenging due to the substantial memory requirements. In this study, we presented a novel model-based deep learning approach, LMPDNet, for TOF-PET reconstruction from list-mode data. We addressed the issue of real-time parallel computation of the projection matrix for list-mode data, and proposed an iterative model-based module that utilized a dedicated network model for list-mode data. Our experimental results indicated that the proposed LMPDNet outperformed traditional iteration-based TOF-PET list-mode reconstruction algorithms. Additionally, we compared the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.

关键词： TOF-PET reconstruction list-mode model-based deep learning

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Heart Segmentation on PA Chest X-ray Images by model-based deep learning Approach

Heart Segmentation on PA Chest X-ray Images by Model-Based D...

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IEEE International Symposium on Medical Measurements and Applications (MeMeA)

作者： Tumay, Adam Hadhazi, Daniel Hullam, Gabor Gamax Lab Solut Budapest Hungary Budapest Univ Technol & Econ Dept Measurement & Informat Syst Budapest Hungary

ISBN: (纸本)9798350308006;9798350307993

Nowadays, Cardivascular Diseases (CVDs) are the most common cause of deaths worldwide. Some lesions connected to CVDs (such as Cardiomegaly, pleural effusion, pulmonary edema) can be detected on acquired Posterior Anterior (PA) chest X-ray projection images (one of the cheapest and most common modality in radiology). By developing robust methods for the segmentation of the heart as a computer aided diagnostic system (CADx) the automatic screening for such lesions becomes possible. In this paper we present a novel model-based deep learning approach by combining Active Shape models (ASM), deep Convolutional Neural Networks (CNN) and expert image processing knowledge through numerical optimization solved by Alternating Direction Method of Multipliers (ADMM) based optimization scheme. The proposed method is evaluated on various datasets against a purely neural network-based approach that is architecturally similar to the proposed solution. Results indicate that our model is more robust, provides a better generalization across datasets especially when segmentation is challenging due to lesions obscuring the target area. Comparison is done on the JSRT dataset as well as on a private dataset containing samples acquired in daily medical practice. Quantitatively, the proposed method achieved a Dices Score of 0.877 +/- 0.05 and a Hausdorff distance of 17.35 mm +/- 4.52 mm.

关键词： medical image processing model-based deep learning computer aided diagnostics heart segmentation active shape models dense UNet alternating direction method of multipliers

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learning From Noisy Data: An Unsupervised Random Denoising Method for Seismic Data Using model-based deep learning

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Wang, Feng Yang, Bo Wang, Yuqing Wang, Ming Zhejiang Univ Sch Earth Sci Key Lab Geosci Big Data & Deep Resource Zhejiang Hangzhou 310027 Peoples R China Tsinghua Univ Inst Artificial Intelligence THUAI Beijing Natl Res Ctr Informat Sci & Technol BNRis State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Poyang Cty Bur Water Resources Off Water Survey & Design Poyang 333100 Peoples R China

For seismic random noise attenuation, deep learning has attracted much attention and achieved promising performance. However, compared with conventional methods, the denoising performance of supervised learning-based methods heavily depends on massive training samples with high-quality labeled data, which makes their generalization capabilities limited. Even though deep neural networks (DNNs) usually outperform the conventional denoising methods, their performance is not guaranteed since neural networks still lack good mathematical interpretability at present. To alleviate the dependency on labeled data and explore insights into the denoising system, we proposed an unsupervised denoising method based on model-based deep learning, which combined domain knowledge and a data-driven method. We designed a network based on the modified iterative soft threshold algorithm (ISTA), which omitted the soft threshold to alleviate uncertainties introduced by empirically selected thresholds. In this network, we set the dictionary and code as trainable parameters. A loss function with a smooth penalty was designed to ensure that the network training can be implemented in an unsupervised manner. In the proposed method, we set the denoised result by f-x deconvolution as the input for our network, and the further denoised data can be obtained after each epoch of the training, which means that our method does not need the testing procedure. Experiments on synthetic and field seismic data demonstrate that our method exhibits competitive performance compared to the conventional, supervised, and unsupervised methods, including f-x deconvolution, curvelet, the Denoising Convolutional Neural Network (DnCNN), and the integration of neural network and Block-matching and 3-D filtering method (NN + BM3D).

关键词： Denoising model-based deep learning random noise seismic data unsupervised

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MoDL-QSM: model-based deep learning for quantitative susceptibility mapping

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NEUROIMAGE 2021年 240卷 118376-118376页

作者： Feng, Ruimin Zhao, Jiayi Wang, He Yang, Baofeng Feng, Jie Shi, Yuting Zhang, Ming Liu, Chunlei Zhang, Yuyao Zhuang, Jie Wei, Hongjiang Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai Peoples R China Shanghai Univ Sport Sch Psychol Shanghai Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai Peoples R China Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Helen Wills Neurosci Inst Berkeley CA 94720 USA ShanghaiTech Univ Sch Informat & Sci & Technol Shanghai Peoples R China Shanghai Jiao Tong Univ Inst Med Robot Shanghai Peoples R China

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (X-13 , X-23 and X-33) and the acquired single-orientation phase. The convolutional neural networks are embedded into the physical model to learn a regularization term containing prior information. X-33 and phase induced by X-13 and X-23 terms were used as the labels for network training. Quantitative evaluation metrics were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications.

关键词： MRI - magnetic resonance imaging QSM - quantitative susceptibility mapping Dipole inversion model-based deep learning deep convolutional neural networks

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deep Unfolding-based Weighted Averaging for Federated learning under Device and Statistical Heterogeneous Environments

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IEICE TRANSACTIONS ON COMMUNICATIONS 2025年第4期E108B卷 411-420页

作者： Nakai-kasai, Ayano Wadayama, Tadashi Nagoya Inst Technol Grad Sch Engn Nagoya 4668555 Japan

Federated learning is a collaborative model training method that iterates model updates by multiple clients and aggregation of the updates by a central server. Device and statistical heterogeneity of participating clients cause significant performance degradation so that an appropriate aggregation weight should be assigned to each client in the aggregation phase of the server. To adjust the aggregation weights, this paper employs deep unfolding, which is known as the parameter tuning method that leverages both learning capability using training data like deep learning and domain knowledge. This enables us to directly incorporate the heterogeneity of the data and environment of interest into the tuning of the aggregation weights. The proposed approach can be combined with various federated learning algorithms. The results of numerical experiments indicate that a higher test accuracy for unknown class-balanced data can be obtained with the proposed method than that with conventional heuristic weighting methods. The proposed method can handle large-scale learning models with the aid of pretrained models such that it can perform practical real-world tasks. Convergence rate of federated learning algorithms with the proposed method is also provided in this paper.

关键词： federated learning deep unfolding model-based deep learning

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Online Meta-learning for Hybrid model-based deep Receivers

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第10期22卷 6415-6431页

作者： Raviv, Tomer Park, Sangwoo Simeone, Osvaldo Eldar, Yonina C. Shlezinger, Nir Ben Gurion Univ Negev Sch ECE IL-8410501 Beer Sheva Israel Kings Coll London Dept Engn London WC2R 2LS England Weizmann Inst Sci Fac Math & CS IL-7610001 Rehovot Israel

Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without relying on knowledge of the channel model. However, the dynamic nature of communication channels often leads to rapid distribution shifts, which may require periodically retraining. This paper formulates a data-efficient two-stage training method that facilitates rapid online adaptation. Our training mechanism uses a predictive meta-learning scheme to train rapidly from data corresponding to both current and past channel realizations. Our method is applicable to any deep neural network (DNN)-based receiver, and does not require transmission of new pilot data for training. To illustrate the proposed approach, we study DNN-aided receivers that utilize an interpretable model-based architecture, and introduce a modular training strategy based on predictive meta-learning. We demonstrate our techniques in simulations on a synthetic linear channel, a synthetic non-linear channel, and a COST 2100 channel. Our results demonstrate that the proposed online training scheme allows receivers to outperform previous techniques based on self-supervision and joint-learning by a margin of up to 2.5 dB in coded bit error rate in rapidly-varying scenarios.

关键词： Wireless communications model-based deep learning deep receivers meta-learning

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