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arXiv 2021年

作者： Paul, Joseph Suresh Madhusoodhanan, Sreekanth Medical Image Computing and Signal Processing Laboratory Indian Institute of Information Technology and Management- Kerala Trivandrum695581 India

This article aims at developing a model based optimization for reduction of temporal unwrapping and field estimation errors in multi-echo acquisition of Gradient Echo (GRE) sequence. Using the assumption that the phase is linear along the temporal dimension, the field estimation is performed by application of unity rank approximation (URA) to the Hankel matrix formed using the complex exponential of the channel-combined phase at each echo time. For the purpose of maintaining consistency with the observed complex data, the linear phase evolution (LPE) model is formulated as an optimization problem with a cost function that involves a fidelity term and a unity rank prior, implemented using alternating minimization. Itoh's algorithm applied to the multi-echo phase estimated from this LPE model is able to reduce the unwrapping errors as compared to the unwrapping when directly applied to the measured phase. Secondly, the improved accuracy of the frequency fit in comparison to estimation using weighted least-square regression (wLSR) and penalized maximum likelihood (pML) is demonstrated using numerical simulation of field perturbation due to magnetic susceptibility effect. It is shown that the field can be estimated with 80% reduction in mean absolute error (MAE) in comparison to wLSR and 66% reduction with respect to pML. The improvement in performance becomes more pronounced with increasing strengths of field gradient magnitudes and echo spacing. © 2021, CC BY.

关键词： Matrix algebra

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Adaptive fast composite splitting algorithm for MR image reconstruction 1

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2nd International Conference on Machine Intelligence and signal processing, MISP 2019

作者： Mathew, Raji Susan Paul, Joseph Suresh Medical Image Computing and Signal Processing Lab Indian Institute of Information Technology and Management TrivandrumKerala India

ISBN: (数字)9789811513664

ISBN: (纸本)9789811513657

Reconstruction of an image from undersampled k-space data using conventional sparsity priors use any of the pre-specified transforms like wavelet, which utilizes the global sparsity of images. Alternatively, methods such as block-matching and 3D filtering (BM3D) utilize the non-local sparsity of images, resulting in better preservation of finer details. However, use of either of the methods cannot utilize global and non-local sparsity at the same time. In this paper, we propose an efficient combination of the global and non-local sparsity realized using wavelet and BM3D, implemented using the fast composite splitting denoising (FCSD) algorithm. Furthermore, we use an adaptive regularization parameter for the wavelet to yield the best achievable performance using adaptive thresholded Landweber (ATL). The proposed adaptive fast composite splitting denoising algorithm (AFCSD) combines the advantages of both BM3D with adaptive TL through the use of FCSD and thus exploits the self-repetitive structure in images by enforcing non-local sparsity as well as entire consistency of the image. Experimental results reveal that the proposed method is superior to any of the individual methods in terms of reconstruction accuracy and computation complexity. Additionally, when used as an auxiliary constraint in dictionary learning (DL), this can result in further improvements in reconstruction with significant reduction in the computation time as compared to a conventional DL reconstruction. © Springer Nature Singapore Pte Ltd. 2020.

关键词： image reconstruction

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The potential of convolutional neural network diagnosing prostate cancer

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Research on Biomedical Engineering 2021年第1期37卷 25-31页

作者： Kudo, Maíra Suzuka de Souza, Vinicius Meneguette Gomes de Souza Amaral, Gabriel de Souza Melo, Petrônio Augusto Estivallet, Carmen Liane Neubarth Santos, Eric Rocha de Amorim, Henrique Alves Moraes, Matheus Cardoso Leite, Katia Ramos Moreira Laboratory of Image and Signal Processing of the Institute of Science and Technology Federal University of São Paulo – UNIFESP 330 Talim St. Room 108 - Jardim Aeroporto Sao Jose dos CamposSP12231-280 Brazil Laboratory of Medical Investigations Number 55 Sao Paulo University Medical School – FMUSP Dr Arnaldo Avenue 455 Room 2145 – Cerqueira Cesar Sao PauloSP01246-903 Brazil

Introduction: There were more than 1,276,106 new cases of prostate cancer (PC) in 2018 worldwide (GLOBOCAN). Early and precise diagnosis leads to cure chances up to 90%. Digital rectal examination and PSA serum levels are employed for prostate cancer screening. If both exams are suspicious for cancer, the patient will be submitted to a prostate biopsy. Histological diagnosis and grading are crucial to the proper manage of the patients and are not always easy to evaluate, demanding experience of pathologists. To test the possibility to adopt artificial intelligent to diagnose PC, we studied a set of prostate biopsy sample images that were input to a specifically constructed convolutional neural network. Purpose: Evaluate the potential of the convolutional neural network for the classification of cancer and non-cancer patches extracted from prostate biopsy images. Methods: Thirty-two prostate cancer biopsy images were obtained and reviewed by a single uropathologist and then transformed into 2594 fragments to feed the CNN. The methodology has been divided into clinical approaches—to extract patches—and computational approaches—the CNN implementation. Results: The k-fold three-way cross-validation method was used, resulting in a 98.3% output accuracy in distinguishing cancer from non-cancer. Conclusion: The presented method proved to be robust and trustworthy comparing with an expert pathologist report. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Convolutional neural networks

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Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional Neural Networks

arXiv

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arXiv 2024年

作者： Wu, Fuzhi Wu, Jiasong Kong, Youyong Yang, Chunfeng Yang, Guanyu Shu, Huazhong Carrault, Guy Senhadji, Lotfi Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Bangladesh Laboratoire Traitement du Signal et de l'Image Univ Rennes France Centre de Recherche en Information Biomédicale Sino-français CRIBs France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Bangladesh

Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite the promising performance of transformer structures in numerous tasks, their intricate optimization complexities highlight the persistent need for refined CNN enhancements using limited resources. Responding to these complexities, we introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keeping their complexity unchanged. The MLFM efficiently preserves low-frequency information, enhancing performance in targeted computer vision tasks. Central to our MLFM is the Low-Frequency Memory Unit (LFMU), which stores various low-frequency data and forms a parallel channel to the core network. A key advantage of MLFM is its seamless compatibility with various prevalent networks, requiring no alterations to their original core structure. Testing on imageNet demonstrated substantial accuracy improvements in multiple 2D CNNs, including ResNet, MobileNet, EfficientNet, and ConvNeXt. Furthermore, we showcase MLFM's versatility beyond traditional image classification by successfully integrating it into image-to-image translation tasks, specifically in semantic segmentation networks like FCN and U-Net. In conclusion, our work signifies a pivotal stride in the journey of optimizing the efficacy and efficiency of CNNs with limited resources. This research builds upon the existing CNN foundations and paves the way for future advancements in computer vision. Our codes are available at https://***/AlphaWuSeu/MLFM. © 2024, CC BY-NC-ND.

关键词： Computer vision

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Optimal Training Positive Sample Size Determination for Deep Learning Caries Recognition Model Establishment with Cbct images

SSRN

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SSRN 2024年

作者： Wang, Yanlin Li, Gang Zhang, Xinyue Wang, Yue Zhang, Zhenhao Li, Jupeng Ma, Junqi Wang, Lingxing Department of Oral and Maxillofacial Radiology Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Device Beijing Key Laboratory of Digital Stomatology NHC Key Laboratory of Digital Stomatology Beijing China Signal and image processing laboratory School of Electronic Information Engineering Beijing Jiao tong University Beijing China Algorithm Department YOFO Medical Technology Co. Ltd Anhui China

Background and objective When performing model training, it is necessary to consider the balance between the impact of sample size, actual resource and time constraints. To do so, the single-arm objective performance criteria for deep learning optimal training positive sample size determination was proposed for caries recognition and identified with three network models. Methods The expected sensitivity of 0.6 and specificity of 0.85 were applied to the single-arm objective performance criteria calculation formula, resulting in an optimal training set with 263 positive samples. According to a ratio of 8:1:1 for the distribution of training set, validation set and test set, the U-Net, YOLOv5n and CariesDetectNet were trained and validated on the clinically self-collected cone-beam computed tomography (CBCT) images. To evaluate performance, an additional set of test sets was used to test the three models. The accuracy of the three best-performed models for caries detection was evaluated through comparisons with the detection accuracy of caries by two dental radiologists. Results When the number of carious teeth reached about 250, the models reached the optimal level of performance. Among the three models, the U-Net performed best, with accuracy, sensitivity, specificity, F1-Score, Dice similarity coefficients, and intersection over union of 0.9929, 0.9307, 0.9989, 0.9590, 0.9435 and 0.9008, respectively. And the three models performed higher accuracy than dental radiologists for caries recognition. Conclusions This study indicated that the optimal number of images with caries in the training set was predictable and it could be calculated through the single-arm objective performance criteria. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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An alternated inertial general splitting method with linearization for the split feasibility problem

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Optimization 2023年第10期72卷 2585-2607页

作者： Qiao-Li Dong Lulu Liu Xiaolong Qin Jen-Chih Yao a Tianjin Key Laboratory for Advanced Signal Processing and College of Science Civil Aviation University of China Tianjin People's Republic of China b Department of Mathematics Hangzhou Normal University Hangzhou People's Republic of China c Research Center for Interneural Computing China Medical University Hospital China Medical University Taichung Taiwand Department of Applied Mathematics National Sun Yat-sen University Kaohsiung Taiwan

In this paper, we propose an alternated inertial general splitting method with linearization for a split feasibility problem. Four rules of inertial parameters and relaxation parameters are discussed, where the adaptive inertial parameters are firstly investigated. The convergence of the proposed method is established under standard conditions. The numerical examples are presented to test four choices of the parameters and illustrate the advantage of our methods. An application in signal processing is presented to illustrate the recovery of the proposed algorithm for the sparse signal.

关键词： Split feasibility problem alternated inertial general splitting method with linearization general splitting methods with linearization alternated inertial CQ algorithm 47H05 47J20 47J25 65K15 90C25

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Multi-Granularity Prosodic Speech Synthesis with Grammar Information

SSRN

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SSRN 2024年

作者： Zhou, Jian Liu, Jiahui Fan, Cunhang Zheng, Wenming Lv, Zhao Tao, Liang Kwan, Hon Keung The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering University of Windsor WindsorN9B 3P4 Canada

Personalized speech synthesis involves learning an individual's unique speaking patterns from a reference audio to generate speech that mimics the speaker's habitual rhythm. However, the one-to-many mapping between text and speech, combined with the dynamic nature of personalized speech style characteristics in the reference audio, poses significant challenges for accurate personalized speech synthesis. In this paper, we propose a Grammar Infused Multi-granularity Prosody Network (GMG ProsodyNet) for personalized speech synthesis. Specifically, 1) We model prosodic features hierarchically at the levels of utterance, content syntax, word, and phoneme. We utilize the utterance-level prosodic feature to guide the prediction of fine-grained prosodic features according to the hierarchical property of prosodic features. We introduce a context syntax encoder to improve the prediction accuracy of duration, pitch, and energy characteristics in the synthesized audio. The proposed word-level prosodic encoder can efficiently extract valuable pitch dynamic and speech continuity features from the word spectrum. For controlling subtle prosodic nuances, we employ phoneme-level prosodic modeling. 2) We extract syntax level prosodic features from the syntax graph constructed by Graph Neural Network (GNN) with word as node, exploiting grammatical dependencies between distant words. Experimental results demonstrate that the proposed GMG ProsodyNet can effectively encode delicate and personalized prosodic features, leading to improved speech synthesis quality, fluency, and naturalness. © 2024, The Authors. All rights reserved.

关键词： Speech synthesis

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Stfi: Structure-Aware and Time-Frequency Domain Interaction Network for Deformable medical image Registration

SSRN

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SSRN 2025年

作者： Hu, Rui Wang, Nian Zhang, Yuxin Li, Teng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Advanced Imaging Research Center University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas75390 United States School of Cyber Science and Engineering Southeast University No.2 Southeast University Road Nanjing211189 China

Deformable image registration is crucial in medical image analysis. Recently, unsupervised methods using intensity matching have shown promising results without labeled data. However, these methods still face challenges: (1) They often encounter local optima when handling anatomical structures with significant variations in intensity distribution;(2) Time domain convolution in decoders leads to loss of high-frequency fine features, limiting the effectiveness of generating fine deformation vector fields. To address these issues, we propose a novel structure-aware and time-frequency domain interaction (STFI) network. STFI integrates a structure-aware frequency domain loss and a time-frequency domain interaction module. The former leverages principal structural information from the weighted phase spectrum to guide the registration process, thereby reducing the risk of local optima. The latter processes and amalgamates information from both the time and frequency domains, effectively preserving high-frequency details. The efficacy and adaptability of our approach have been demonstrated on two types of data, offering a new perspective on exploiting time-frequency domain information for medical image registration. © 2025, The Authors. All rights reserved.

关键词： image registration

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Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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