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arXiv 2023年

作者： Lobos, Rodrigo A. Chan, Chin-Cheng Haldar, Justin P. The Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States

This is an unabridged version of a journal manuscript that has been submitted for publication [1]. (Due to length restrictions, we were forced to remove substantial amounts of content from the version that was submitted to the journal, including more detailed theoretical explanations, additional figures, and a more comprehensive bibliography. This content remains intact in this version of the document). Sensitivity map estimation is important in many multichannel MRI applications. Subspace-based sensitivity map estimation methods like ESPIRiT are popular and perform well, though can be computationally expensive and their theoretical principles can be nontrivial to understand. In the first part of this work, we present a novel theoretical derivation of subspace-based sensitivity map estimation based on a linear-predictability/structured low-rank modeling perspective. This results in an estimation approach that is equivalent to ESPIRiT, but with distinct theory that may be more intuitive for some readers. In the second part of this work, we propose and evaluate a set of computational acceleration approaches (collectively known as PISCO) that can enable substantial improvements in computation time (up to ∼100× in the examples we show) and memory for subspace-based sensitivity map estimation. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Lightweight Deep Learning Model for Melanoma Classification in Dermoscopy images for Smart Healthcare

Lightweight Deep Learning Model for Melanoma Classification ...

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International Conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Pentapati Naga Sree Charan Teja Thunakala Bala Krishna Ajay Kumar Reddy Poreddy Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350845

ISBN: (纸本)9798350350852

Among various types of skin cancers, melanoma is the most aggressive and deadly. There is a notable growth in the implementation of deep learning (DL) methods to identify skin malignancies in dermoscopy images. This paper introduces a lightweight DL-based approach designed for seamless integration into low-memory devices within healthcare applications. The proposed method incorporates three lightweight convolutional neural network (CNN) models: MobileNet-v2, SqueezeNet, and GoogLeNet. Initially, test features are computed from fine-tuned deep CNN models. Subsequently, probability scores for each class are derived by training and testing a random forest classifier with features extracted from the models. Then, the proposed method uses an average ensemble voting technique on the probability scores to enhance the classification performance compared to the individual models. The proposed of lightweight CNN model demonstrated an accuracy of 85.19 % which is better than existing works.

关键词： Deep learning Wireless communication Adaptation models Computational modeling Melanoma Medical services Feature extraction

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Enhanced Focal Liver Lesion Classification using Channel Attention Technique

Enhanced Focal Liver Lesion Classification using Channel Att...

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Conference on Information and Communication Technology (CICT)

作者： Sunkanaboina Chandra Lingamaiah Thunakala Bala Krishna Ajay Kumar Reddy Poreddy Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Accurate and timely diagnosis of liver disorders such as fatty liver disease, chronic viral hepatitis, and excessive alcohol consumption is crucial for maintaining liver health. Traditional methods for liver screening are often subjective, time-consuming, and reliant on the expertise of the sonographer, which can impact diagnostic accuracy. To tackle these challenges, in this paper proposed a deep learning (DL) based framework to enhance the effective diagnosis of focal liver lesions. This approach leverages an channel attention mechanism integrated with the DarkNet-19 pre-trained model to improve feature extraction and boosts classification accuracy. By automating the diagnostic process, the proposed model addresses the limitations of traditional methods, providing a more efficient and reliable solution for liver disorder diagnosis. Experimental results with an ultrasound image dataset demonstrate that the proposed model significantly outperforms conventional DL methods, showcasing its advantages in performance and efficiency.

关键词： Frequency locked loops Deep learning Accuracy Ultrasonic imaging Attention mechanisms Liver diseases Feature extraction Information and communication technology Lesions Reliability

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A Computer Aided Detection System for Breast Cancer using Lightweight CNN Models for Smart Healthcare

A Computer Aided Detection System for Breast Cancer using Li...

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International Conference on Biosignals, images and Instrumentation (ICBSII)

作者： Varun C Ajay Kumar Reddy Poreddy Thunakala Balakrishna Priyanka Kokil Department of Electronics and Communication Engineering Advanced Signal and Image Processing (ASIP) Lab Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai

ISBN: (数字)9798350350951

ISBN: (纸本)9798350350968

Breast cancer (BC) is a potentially life-threatening disease that occurs because of uncontrolled growth of abnormal corpuscles in the breast tissue. Pathologists analyze the tissue structures using histopathological whole slide images to identify cancerous anomalies. However, pathologists face severe challenges such as fatigue, subjectivity, and inter-observer variability in the early detection of BC. Understanding the intricacies of BC from molecular tissue structures is complex, and inexpertise leads to adverse outcomes. This paper proposes a computed aided detection (CAD) system that can assist histopathologists in the early detection of BC, potentially reducing the abnormalities and diagnostic time. Leveraging the power of convolutional neural networks (CNNs), a stacked ensemble-based model is developed to identify benign and malignant cancerous tissues using histopathological images. The ensemble models comprise three deep CNNs, namely MobileNetV2, ShuffleNet, and SqueezeNet, trained on the BreakHis dataset. Finally, individual CNNs predictions are fed to the average voting-based classifier to identify benign and malignant tissues. The stacked ensemble-based deep CNN model outperformed the individual CNN models in BC prediction, achieving superior accuracy and robustness.

关键词： Solid modeling Computational modeling Medical services Predictive models Breast cancer Robustness Quality assessment

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Modified ResNet for Volumetric Segmentation of Liver and Its Tumor from 3D CT

Modified ResNet for Volumetric Segmentation of Liver and Its...

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International Conference on Artificial Intelligence and Sustainable engineering, AISE 2020

作者： Mourya, Gajendra Kumar Bhatia, Dinesh Gogoi, Manashjit Talbar, S.N. Baid, Ujjwal Dudante, Prasad Department of Biomedical Engineering North-Eastern Hill University Meghalaya Shillong India Center of Excellence in Signal and Image Processing Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India

ISBN: (纸本)9789811685415

Liver tumor segmentation is the first step to investigate severity of liver disorder. The conventional automatic segmentation methods highly depend on hand-crafted features and priori information, which are tedious and time consuming for medical images. Fully convolutional network (FCN) of the deep learning model can extract features automatically and hierarchically learning concerning layers depth. In this article, we demonstrated the application of modified ResNet for the automatic segmentation of liver and tumor segmentation from 3D CT medical imaging datasets. The FCN ResNet architecture is modified while changing the levels at encoder and decoder ends to overcome the limitation of the conventional segmentation technique. The network input and output image dimension kept the same for training and testing for the designed model. LiTS and 3Dirac data use for training and testing the model. Multifold training datasets strategy utilized. Results were evaluated quantitatively and achieved DICE 0.96 and 0.81 for liver and tumor, respectively. The modified technique will assist to interventional radiologist workflow and faster the diagnostic and therapeutic procedures. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computerized tomography

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Non-invasive glucose sensing via the fingernail bed using THz radiation

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Current Directions in Biomedical engineering 2023年第1期9卷 507-510页

作者： Jalali, Mandana Prokscha, Andreas Kaiser, Thomas Yan, Yijun Taro Svejda, Jan Erni, Daniel Kubiczek, Tobias Balzer, Jan Preu, Sascha General and Theoretical Electrical Engineering (ATE) Faculty of Engineering University of Duisburg-Essen Essen Duisburg Germany CENIDE-Center for Nanointegration Duisburg-Essen Duisburg D-47048 Germany Institute of Digital Signal Processing Faculty of Engineering University of Duisburg-Essen Duisburg D-47048 Germany Chair of Communication Systems Faculty of Engineering University of Duisburg-Essen Duisburg D-47048 Germany Terahertz Devices and Systems Institute of Microwave Engineering and Photonics Department of Electrical Engineering and Information Technology Technical University of Darmstadt Darmstadt D-64283 Germany

The possibility of establishing a novel technique for reliably accessing glycemic information in a non-invasive, easy to implement method at THz frequencies via the fingernail bed is investigated. The nail bed's major content is blood at its various glucose levels and is also partially protected from environmental conditions by the nail plate, making it a desirable platform for non-invasive glucose sensing. The study is based on a 2D computational electromagnetics (EM) model of the layered fingernail in COMSOL Multiphysics, where the required dielectric function (i.e. permittivity and conductivity) of the fingernail plate is measured using THz-Time-Domain-Spectroscopy (THz-TDS), and the glucose-dependend dielectric functions of the fingernail bed are taken from available experimental data in the literature. From this data a material model using a multipole Cole-Cole model is established for both, the nail plate and the nail bed, where the glucose content in the latter is varied from 3.0-19.0 mmol/l. A numerical analysis of the THz reflectometry at the fingernail in the frequency range of 0.1-2.0 THz revealed that the reflectance spectra are sensitive to the glucose content in the nail bed proving that accurate glucose sensing via the fingernail bed may become feasible around 0.2-0.4 THz. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Glucose

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An Efficient Algorithm for Spatial-Spectral Partial Volume Compartment Mapping with Applications to Multicomponent Diffusion and Relaxation MRI

arXiv

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arXiv 2024年

作者： Liu, Yunsong Mandal, Debdut Liao, Congyu Setsompop, Kawin Haldar, Justin P. Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Departments of Radiology and Electrical Engineering Stanford University StanfordCA94305 United States

We introduce a new algorithm to solve a regularized spatial-spectral image estimation problem. Our approach is based on the linearized alternating directions method of multipliers (LADMM), which is a variation of the popular ADMM algorithm. Although LADMM has existed for some time, it has not been very widely used in the computational imaging literature. This is in part because there are many possible ways of mapping LADMM to a specific optimization problem, and it is nontrivial to find a computationally efficient implementation out of the many competing alternatives. We believe that our proposed implementation represents the first application of LADMM to the type of optimization problem considered in this work (involving a linear-mixture forward model, spatial regularization, and nonnegativity constraints). We evaluate our algorithm in a variety of multiparametric MRI partial volume mapping scenarios (diffusion-relaxation, relaxation-relaxation, relaxometry, and fingerprinting), where we consistently observe substantial (∼3×-50×) speed improvements. We expect this to reduce barriers to using spatially-regularized partial volume compartment mapping methods. Further, the considerable improvements we observed also suggest the potential value of considering LADMM for a broader set of computational imaging problems. Copyright © 2024, The Authors. All rights reserved.

关键词： Mapping

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Optimization of Tube Slot Patch Antenna for TeraHertz Systems

Optimization of Tube Slot Patch Antenna for TeraHertz System...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Kumar, D. Sathish Poonguzhali, S. Sivasangari, A. Lalithakumari, S. Rajalakshmi, G. Pandian, R. Sri Eshwar College of Engineering Centre for Advances in Signal Processing Department of Ece Coimbatore India Sathyabama Institute of Science and Technology Department of Ece Chennai India Sathyabama Institute of Science and Technology Department of It Chennai India School of Electrical and Electronics Sathyabama Institute of Science and Technology Department of Ece Chennai India

ISBN: (纸本)9798350315127

In recent decades, there has been a lot of fascination with THz innovation. THz antennas are vital components in wireless communication for transferring and receiving electromagnetic fields in the developing THz system because of their appealing qualities of compact size, wider spectrum, and greater data rate. This paper suggests the unique slotted rectangular patch antenna based on a metamaterial structure and analyses the antenna performances. It has been noted that the traditional antenna's efficiency is really subpar and that the metamaterial structure influences the EM radiation and improves the performance. As a result, the rectangular patch is modified with the metamaterial structure by incorporating a tube slot into the antenna. Furthermore, the features of the patch antenna are studied by optimizing the slot number and thickness. The optimized tube slot patch antenna produces an excellent result of -58.17 dB return loss with a good gain of 8.6 dBi at the operating frequency of 2.37 THz. The proposed typical metamaterial antenna is suitable for wireless devices with greater data rates, THz imaging and medical domains. © 2023 IEEE.

关键词： Metamaterials

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Improving Automatic Sleep Staging Via Temporal Smoothness Regularization 48

Improving Automatic Sleep Staging Via Temporal Smoothness Re...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Phan, Huy Heremans, Elisabeth Chén, Oliver Y. Koch, Philipp Mertins, Alfred De Vos, Maarten Amazon Alexa CambridgeMA United States Ku Leuven Department of Electrical Engineering Belgium University of Bristol School of Economics Finance and Management United Kingdom University of Lübeck Institute for Signal Processing Germany Germany

ISBN: (纸本)9781728163277

We propose a regularization method, so-called temporal smoothness regularization, for training deep neural networks for automatic sleep staging in small data settings. In intuition, we constrain the cross-entropy losses of any two adjacent epochs in the sequential input to be as close to each other as possible. The regularization closely reflects the slow transition nature of sleep process which implies small information changes between two consecutive sleep epochs. Via the regularization, we essentially discourage the network from overfitting to these small changes. Our experiments show that training the SeqSleepNet base network with the proposed regularization leads to performance improvement over the baseline without the regularization applied. Furthermore, our developed method achieves the performance on par with the state-of-the-art performance while outperforming other existing methods. © 2023 IEEE.

关键词： Automatic sleep staging regularization SeqSleepNet temporal smoothness transfer learning

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Bayesian Rule Ontologies For XAI Classification and Regression

Bayesian Rule Ontologies For XAI Classification and Regressi...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Akash Kumar Panda Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute University of Southern California Los Angeles California

A random foam defines a modular rule-based ontology after sampling from a neural or other input-output system. A random foam combines several rule-based systems and averages the systems. It gives a Bayesian posterior over the subsystems. It also gives separate Bayesian posteriors over the rules of each subsystem. The shape of the rules controls how well the random-foam ontology performs in classification and regression. We found that a heterogenous ontology that mixes different rule shapes can perform better than a homogenous ontology based on a single Gaussian or other rule type. Random foams are also universal function approximators. So they can train on a neural black box and act as its explainable proxy system. We prove this uniform approximation theorem for the important case of bump-function random foams with throughput combination. Random foams also measure their output’s uncertainty through the conditional variance. Bump function rules performed better than Cauchy rules at classification while Cauchy rules performed better at regression. Gaussian rules performed best in both classification and regression. A homogeneous Gaussian random foam that trained on a 96.7% accurate neural classifier was itself 95.96% accurate on the MNIST data set. A heterogeneous random foam with two-thirds Gaussian rules and one-third Laplacian rules did better than did the all-Gaussian foam ontology.

关键词： Uncertainty Laplace equations Shape Measurement uncertainty Machine learning Ontologies Aerospace electronics

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