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RETRACTED ARTICLE: A comparative recognition research on excretory organism in medical applications using neural networks

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Multimedia Tools and Applications 2023年 1-2页

作者： K, Vidhya R, Krishna Priya Vidhyalakshmi, M. Ramesh, S. M. L, Bharathi Kshirsagar, Pravin R. Rajaram, A. Tirth, Vineet Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Engineering - Electrical Section University of Technology and Applied Sciences Shinas Campus Sultanate of Oman Department of Electronics and Communication Engineering SRM Institute of Science and Technology Chennai India Department of Signal and Image Processing With Computational Intelligence Saveetha School of Engineering Chennai India Department of Electricals and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India Department of Mechanical Engineering College of Engineering King Khalid University Abha Kingdom of Saudi Arabia

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1D quasi-bound states in the continuum with large operation bandwidth in theω~k space for nonlinear optical applications

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Photonics Research 2022年第7期10卷 1575-1581页

作者： KAILI SUN HUI JIANG DMITRY ABYKOV VIEN VAN URIEL LEVY YANGJIAN CAI ZHANGHUA HAN Shandong Provincial Key Laboratory of Optics and Photonic Devices Center of Light Manipulation and ApplicationsSchool of Physics and ElectronicsShandong Normal UniversityJinan 250358China Image Processing Systems Institute—Branch of the Federal Scientific Research Centre“Crystallography and Photonics”of Russian Academy of Sciences Samara 443001Russia Department of Electrical and Computer Engineering University of AlbertaEdmontonAlberta T6G 2V4Canada Department of Applied Physics and Center for Nanoscience and NanotechnologyThe Hebrew University of JerusalemJerusalemIsrael

The phenomenon of bound state in the continuum(BIC)with an infinite quality factor and lifetime has emerged in recent years in photonics as a new tool to manipulate light-matter ***,most of the investigated structures only support BIC resonances at very few discrete points in theω~k *** when the BIC is switched to a quasi-BIC(QBIC)resonance through perturbation,its frequency will still be located within a narrow spectral band close to that of the original BIC,restricting their applications in many fields where random or multiple input frequencies beyond the narrow band are *** this work,we demonstrate that a new set of QBIC resonances can be supported by using a special binary grating consisting of two alternatingly aligned ridge arrays with the same period and zero-approaching ridge width difference on a slab *** QBIC resonances are distributed continuously over a broad band along a line in theω~k space and can thus be considered as 1 D *** the Q factors generally affected by the ridge difference,it is now possible to arbitrarily choose any frequencies on the dispersion line to achieve significantly enhanced light-matter interactions,facilitating many applications where multiple input wavelengths are required;e.g.,sum or difference frequency generations in nonlinear optics.

关键词： quasi nonlinear narrow

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RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation

arXiv

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arXiv 2022年

作者： Zhu, Guijie Fan, Zhun Liu, Jiacheng Yuan, Duan Ma, Peili Wang, Meihua Sheng, Weihua Wang, Kelvin C.P. College of Engineering Shantou University China Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou515063 China College of Mathematics and Informatics South China Agricultural University Guangzhou501642 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States School of Civil and Environmental Engineering Oklahoma State University StillwaterOK74078 United States

The acquisition and evaluation of pavement surface data play an essential role in pavement condition evaluation. In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy. The RHA-Net is built by integrating residual blocks (ResBlocks) and hybrid attention blocks into the encoder-decoder architecture. The ResBlocks are used to improve the ability of RHA-Net to extract high-level abstract features. The hybrid attention blocks are designed to fuse both low-level features and high-level features to help the model focus on correct channels and areas of cracks, thereby improving the feature presentation ability of RHA-Net. An image data set containing 789 pavement crack images collected by a self-designed mobile robot is constructed and used for training and evaluating the proposed model. Compared with other state-of-the-art networks, the proposed model achieves better performance and the functionalities of adding residual blocks and hybrid attention mechanisms are validated in a comprehensive ablation study. Additionally, a light-weighted version of the model generated by introducing depthwise separable convolution achieves better a performance and a much faster processing speed with 1/30 of the number of U-Net parameters. The developed system can segment pavement crack in real-time on an embedded device Jetson TX2 (25 FPS). The video taken in real-time experiments is released at https://***/3XIogk0fiG4. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR

JACC: Advances

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JACC: Advances 2024年第10期3卷 101249-101249页

作者： Han, Donghee Shanbhag, Aakash Miller, Robert JH. Kwok, Nicholas Waechter, Parker Builoff, Valerie Newby, David E. Dey, Damini Berman, Daniel S. Slomka, Piotr Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada British Heart Foundation Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom

Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results: A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, P < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, P = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, P < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, P < 0.001), with a small bias (−9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR. Conclusions: The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR. © 2024 The Authors

关键词： artificial intelligence cardiac magnetic resonance imaging computed tomography myocardial mass

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Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation

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Scientific reports 2025年第1期15卷 17791页

作者： Sajed Rakhshani Amirali Arbab Aref Habibi Mohsen Pourazizi Mahnoosh Tajmirriahi Farnaz Sedighin Hossein Rabbani Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran. Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran. Department of Ophthalmology Isfahan Eye Research Center Isfahan University of Medical Sciences Isfahan Iran. Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran. rabbani.h@***.

This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in the hybrid integration of data-driven deep-learning with a model-informed noise representation, specifically designed to address the very low signal-to-noise ratio (SNR) and low-resolution challenges in SS-OCT imaging. SDNet introduces a two-step training process, leveraging noise-free OCT references to simulate low-SNR conditions. In the first step, the network learns to enhance noisy images by combining denoising and super-resolution within noise-corrupted reference domain. To refine its performance, the second step incorporates Principle Component Analysis (PCA) as self-supervised denoising strategy, eliminating the need for ground-truth noisy image data. This unique approach enhances SDNet's adaptability and clinical relevance. A key advantage of SDNet is its ability to balance contrast-texture by adjusting the weights of the two training steps, offering clinicians flexibility for specific diagnostic needs. Experimental results across diverse datasets demonstrate that SDNet surpasses traditional model-based and data-driven methods in computational efficiency, noise reduction, and structural fidelity. The framework excels in improving both image quality and diagnostic accuracy. Additionally, SDNet shows promising adaptability for analyzing low-resolution, low-SNR OCT images, such as those from patients with diabetic macular edema (DME). This study establishes SDNet as a robust, efficient, and clinically adaptable solution for OCT image enhancement addressing critical limitations in contemporary imaging workflows.

关键词： Denoising Low-SNR OCT Model-aware deep learning Self-supervised learning Super-resolution

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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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Deep Learning-Enabled, Computed Tomography-Based Race- and Sex-Specific Epicardial Adipose Tissue Thresholds for Cardiovascular Risk Stratification

Deep Learning-Enabled, Computed Tomography-Based Race- and S...

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Medical Imaging 2025: Clinical and Biomedical Imaging

作者： Buchwald, Mikolaj Shanbhag, Aakash Miller, Robert J.H. Zhang, Wenhao Michalowska, Anna M. Killekar, Aditya Lemley, Mark Kavanagh, Paul B. Fujito, Hidesato Liang, Joanna X. Builoff, Valerie Knight, Stacey Einstein, Andrew J. Miller, Edward J. Feher, Atilla Sinusas, Albert J. Chareonthaitawee, Panithaya Bullock-Palmer, Renee P. Di Carli, Marcelo F. Berman, Daniel S. Dey, Damini Slomka, Piotr J. Departments of Medicine Division of Artificial Intelligence in MedicineImaging and Biomedical Sciences Cedars-Sinai Medical Center Los AngelesCA United States Poznan Supercomputing and Networking Center Polish Academy of Sciences Poznan Poland Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Department of Cardiac Sciences University of Calgary CalgaryAB Canada Center of Radiological Diagnostics National Medical Institute The Ministry of the Interior and Administration Warsaw Poland Department of Cardiology Nihon University Itabashi Hospital Tokyo Japan Intermountain Medical Center Heart Institute MurrayUT United States Division of Cardiology Columbia University Irving Medical Center New York CityNY United States Section of Cardiovascular Medicine Department of Internal Medicine Yale University School of Medicine New HavenCT United States Department of Cardiovascular Medicine Mayo Clinic RochesterMN United States Department of Cardiology Deborah Heart and Lung Center Browns MillsNJ United States Division of Cardiovascular Medicine Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Cardiovascular Imaging Program Departments of Radiology and Medicine Division of Nuclear Medicine and Molecular Imaging Department of Radiology Brigham and Women’s Hospital Harvard Medical School BostonMA United States

ISBN: (纸本)9781510685987

While the volume of epicardial adipose tissue (EAT) has been linked to various conditions and showed a prognostic value of cardiovascular events, an insufficient effort was put into proposing clear, race- and sex-specific thresholds of high-risk and low-risk EAT amounts. Also, previous works focused on the absolute volume of EAT in milliliters, adjusted to the body size of the patients, and none has proposed a relative measure of what is the amount of EAT for a given patient also in reference to the size of the patient’s heart. In this retrospective study, computed tomography attenuation correction (CTAC) scans from 20,587 patients (58% male;84% White, 13% Black, 3% Asian) from 6 sites were used. With the previously described deep learning (DL)-based method, EAT values per patient were calculated, and subsequently normalized using either the DL-obtained cardiac volume mask, body size, or both. The association between the percent of EAT in reference to the heart size and incident mortality or nonfatal myocardial infarction (MI) was evaluated with Cox models adjusted for age, sex, body mass index, race, cigarette smoking status, hypertension, diabetes, family history of coronary artery disease, dyslipidemia, and left ventricle size. In total, 4,133 events were observed over a period of 6-year follow-up. Our analysis showed that when the amount of EAT was normalized jointly for body surface area and heart size, the predictive effect of EAT was diminished (p=0.85). Patients with higher EAT density, however, were more likely to experience death or MI during follow-up (p © 2025 SPIE.

关键词： Risk assessment

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Blip-Up Blip-Down Circular EPI (BUDA-cEPI) for Distortion-Free dMRI with Rapid Unrolled Deep Learning Reconstruction

arXiv

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arXiv 2023年

作者： Yarach, Uten Chatnuntawech, Itthi Liao, Congyu Teerapittayanon, Surat Iyer, Siddharth Srinivasan Kim, Tae Hyung Haldar, Justin Cho, Jaejin Bilgic, Berkin Hu, Yuxin Hargreaves, Brian Setsompop, Kawin Radiologic Technology Department Associated Medical Sciences Chiang Mai University Chiang Mai Thailand National Nanotechnology Center National Science and Technology Development Agency Pathum Thani Thailand Department of Radiology Stanford University StanfordCA United States Department of Electrical Engineering Stanford University StanfordCA United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Computer Engineering Hongik University Seoul Korea Republic of Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States Department of Bioengineering Stanford University StanfordCA United States Packard Electrical Engineering Building 350 Jane Stanford Way StanfordCA94305 United States

Purpose: We implemented the blip-up, blip-down circular echo planar imaging (BUDA-cEPI) sequence with readout and phase partial Fourier to reduced off-resonance effect and T2* blurring. BUDA-cEPI reconstruction with S-based low-rank modeling of local k-space neighborhoods (S-LORAKS) is shown to be effective at reconstructing the highly under-sampled BUDA-cEPI data, but it is computationally intensive. Thus, we developed an ML-based reconstruction technique termed "BUDA-cEPI RUN-UP" to enable fast reconstruction. Methods: BUDA-cEPI RUN-UP – a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also incorporated into the reconstruction to effectively take advantage of the smooth phase prior, and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS. Results: BUDA-cEPI with S-LORAKS reconstruction enabled the management of off-resonance, partial Fourier, and residual aliasing artifacts. However, the reconstruction time is approximately 225 seconds per slice, which may not be practical in a clinical setting. In contrast, the proposed BUDA-cEPI RUN-UP yielded similar results to BUDA-cEPI with S-LORAKS, with less than a 5% normalized root mean square error detected, while the reconstruction time is approximately 3 seconds. Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ~88x when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Hierarchical and Multimodal Data for Daily Activity Understanding

arXiv

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arXiv 2025年

作者： Kaviani, Ghazal Yarici, Yavuz Kim, Seulgi Prabhushankar, Mohit AlRegib, Ghassan Solh, Mashhour Patil, Ameya OLIVES Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Amazon Lab126 San FranciscoCA United States

Daily Activity Recordings for artificial intelligence (DARai, pronounced /Dahr-ree/), is a multimodal, hierarchically annotated dataset constructed to understand human activities in real-world settings. DARai consists of continuous scripted and unscripted recordings of 50 participants in 10 different environments, totaling over 200 hours of data from 20 sensors including multiple camera views, depth and radar sensors, wearable inertial measurement units (IMUs), electromyography (EMG), insole pressure sensors, biomonitor sensors, and gaze tracker. To capture the complexity in human activities, DARai is annotated at three levels of hierarchy: (i) high-level activities (L1) that are independent tasks, (ii) lower-level actions (L2) that are patterns shared between activities, and (iii) fine-grained procedures (L3) that detail the exact execution steps for actions. The dataset annotations and recordings are designed so that 22.7% of L2 actions are shared between L1 activities and 14.2% of L3 procedures are shared between L2 actions. The overlap and unscripted nature of DARai allows counterfactual activities in the dataset. Experiments with various machine learning models showcase the value of DARai in uncovering important challenges in human-centered applications. Specifically, we conduct unimodal and multimodal sensor fusion experiments for recognition, temporal localization, and future action anticipation across all hierarchical annotation levels. To showcase the shortcomings of individual sensors, we conduct domain-variant experiments that are possible because of DARai’s multi-sensor and counterfactual activity design setup. The code, documentation, and dataset is available at the dedicated DARai website. © 2025, CC BY.

关键词： image segmentation

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A comparative recognition research on excretory organism in medical applications using neural networks

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Multimedia Tools and Applications 2023年

作者： K, Vidhya R, Krishna Priya Vidhyalakshmi, M. Ramesh, S. M. L, Bharathi Kshirsagar, Pravin R. Rajaram, A. Tirth, Vineet Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Engineering - Electrical Section University of Technology and Applied Sciences Shinas Campus Oman Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Signal and Image Processing With Computational Intelligence Saveetha School of Engineering Tamil Nadu Chennai600124 India Department of Electricals and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Artificial Intelligence G H Raisoni College of Engineering Nagpur India Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

In the current generation, most individuals are facing many health problems even in the elderly stages and one of the severe touch is caused by to digestive system. Usually, the problems in the digestive system transpire due to improper innards that are present inside it. Even though many experts have described effective solutions to overcome such problems no proper outcomes are achieved. Therefore, the proposed method builds an appropriate device that detects the harmful contents in the entire digestive system which records and stores the data for a continuous period. Additionally, the designed device is incorporated with the neural network using five defined layers where the data is divided into mini segments. This type of data segmentation of input images will reduce the amount of loss in the system thus making the system to be operated at reduced cost. Further equal weights are allocated in the hidden layer using reduced control parameters thereby achieving concentrated attack at several periods. To analyze the effectiveness of neural networks parametric cases are performed and the results prove to be very effective in comparison cases for about 68 percentage. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Medical applications

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