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model-based deep learning Algorithm for Detection and Classification at High Event Rates

IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2024年第5期71卷 970-980页

作者： Morad, Itai Ghelman, Max Ginzburg, Dimitry Osovizky, Alon Shlezinger, Nir Ben Gurion Univ Negev Sch ECE IL-84105 Beer Sheva Israel Israel Atom Energy Commiss IL-61070 Tel Aviv Israel Nucl Res Ctr Elect & Control Labs IL-84190 Negev Israel Rotem Ind Ltd Hlth Phys Instrumentat Dept IL-85339 Lehavim Israel Nucl Res Ctr Elect & Control Labs Beer Sheva Israel

Pulse shape discrimination (PSD) is required for many radioactive particle monitoring applications. Classical PSD methods commonly struggle at high event rates in the presence of pile up, and are therefore utilized for low event rates. We present a PSD algorithm that combines classic approaches with deep learning techniques. The algorithm provides both detection and classification of the pulses at high event rates. While PSD algorithms for high event rates are often limited to two piled-up pulses, our algorithm is designed and tested for detection and classification under severe pile-up conditions, where three or more pulses are piled up. The algorithm was tested on both experimental data from a Cs(2)LiYCl6:Ce (CLYC) detector and on synthetic data. The algorithm's detection and discrimination performance is compared to current state-of-the-art methods. The algorithm's performance is characterized under varying event rates, signal-to-noise ratio (SNR) conditions, and neutron-to-gamma event rate ratios.

关键词： Cs(2)LiYCl6:Ce (CLYC) high rate model-based deep learning pulse pile up pulse shape discrimination (PSD)

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model-based deep learning for Distributed Maneuvering Target Tracking 17th

Model-Based Deep Learning for Distributed Maneuvering Target...

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17th International Conference on Intelligent Robotics and Applications

作者： Yang, Feng Gao, Tongyang Zheng, Litao Liao, Pan Northwestern Polytech Univ Sch Automat Xian Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (纸本)9789819607853;9789819607860

Traditional model-based distributed maneuvering target tracking methods often require prior knowledge of both sensor and target models, which can lead to significant degradation in tracking accuracy when faced with model mismatches. To address this problem, this paper presents a novel model-based deep learning distributed maneuvering target tracking method over a homogeneous sensor network. Specifically, two deep neural networks are designed to obtain noise-eliminated measurements and timely estimate the motion model of the target. Further, hybrid consensus on measurements and consensus on information method is utilized to fuse local filtering results. Finally, the simulation experiment demonstrates the effectiveness of the proposed method.

关键词： Distributed Sensor Network model-based deep learning Maneuvering Target Tracking

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Efficient model-based deep learning via Network Pruning and Fine-Tuning

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2025年第2期67卷 1-14页

作者： Park, Chicago Y. Gan, Weijie Zou, Zihao Hu, Yuyang Sun, Zhixin Kamilov, Ulugbek S. Washington Univ St Louis Dept Comp Sci & Engn 1 Brookings Dr St Louis MO 63130 USA Washington Univ St Louis Dept Elect & Syst Engn 1 Brookings Dr St Louis MO 63130 USA

model-based deep learning (MBDL) is a powerful methodology for designing deep models to solve imaging inverse problems. MBDL networks can be seen as iterative algorithms that estimate the desired image using a physical measurement model and a learned image prior specified using convolutional neural networks (CNNs). The iterative nature of MBDL networks increases the test-time computational complexity, which limits their applicability in certain large-scale applications. Here we make two contributions to address this issue: First, we show how structured pruning can be adopted to reduce the number of parameters in MBDL networks. Second, we present three methods to fine-tune the pruned MBDL networks to mitigate potential performance loss. Each fine-tuning strategy has a unique benefit that depends on the presence of a pre-trained model and a high-quality ground truth. We show that our pruning and fine-tuning approach can accelerate image reconstruction using popular deep equilibrium learning (DEQ) and deep unfolding (DU) methods by 50% and 32%, respectively, with nearly no performance loss. This work thus offers a step forward for solving inverse problems by showing the potential of pruning to improve the scalability of MBDL. Code is available at https://***/wustl-cig/MBDL_Pruning.

关键词： Computational imaging Inverse problem model-based deep learning Network pruning

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

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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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model-based deep learning for Computational Imaging

Model-Based Deep Learning for Computational Imaging

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作者： Xu, Xiaojian Washington University in St. Louis

学位级别：Ph.D., Doctor of Philosophy

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis. The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized by denoising (RED) and deep unfolding (DU), all of which integrate model-based optimization and deep learning. PnP and RED adopt deep-learned denoisers as image priors inside model-based iterative algorithms, while DU interprets the iterations of a model-based algorithm as layers of a deep neural network and trains it end-to-end in a supervised fashion. We contribute to these research areas by 1) providing the statistical interpretation of the PnP algorithms through the analysis of the priors implicitly represented by denoisers; 2) proposing an incremental variant of the widely-used PnP-ADMM algorithm to handle problems involving large-scale measurements; 3) extending the family of PnP algorithms to the non-Euclidean setting based on the general Bregman distance; and 4) developing an end-to-end model-based learning framework for the estimation of quantitative maps from under-sampled, noisy and motion-corrupted MRI data.

关键词： Computational imaging deep unfolding model-based deep learning Optimization Plug-and-play priors Regularized by denoising

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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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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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model-based deep learning for additive manufacturing: New frontiers and applications

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MANUFACTURING LETTERS 2021年 29卷 94-98页

作者： Ghungrad, Suyog Gould, Benjamin Soltanalian, Mojtaba Wolff, Sarah Jeannette Haghighi, Azadeh Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA Argonne Natl Lab Appl Mat Div 9700 S Cass Ave Lemont IL 60439 USA Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Texas A&M Univ Ind & Syst Engn College Stn TX 77843 USA

Artificial intelligence has created disruptive possibilities in additive manufacturing towards smarter design, process control, and quality assurance. Nonetheless, the scarcity of data in additive manufacturing significantly limits the wide adoption of artificial intelligent techniques. In this work, we propose the deployment of a novel artificial intelligent structure called model-based deep learning in the context of additive manufacturing which can address scenarios with scarce data but an available underlying iterative mathematical/inference model. Several immediate applications of this technique in the additive manufacturing research as well as a proof of concept on temperature profile prediction in metal AM process are presented. (C) 2021 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.

关键词： Additive manufacturing model-based deep learning Artificial intelligence Data scarcity Process and material variability

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