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Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images (Physical and engineering Sciences in Medicine, (2024), 10.1007/s13246-024-01420-1)

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Physical and engineering Sciences in Medicine 2025年第1期48卷 19-20页

作者： Mostafavi, Mohammadreza Ko, Seok-Bum Shokouhi, Shahriar Baradaran Ayatollahi, Ahmad Department of Electrical Engineering Electronic Engineering Iran University of Science and Technology Tehran Iran Division of Biomedical Engineering Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship" to "The generic teacher-student framework for knowledge distillation [33]." In Sharma et al.’s research, two results obtained from the "KNN with ten-fold cross-validation" and "KNN with leave-one-subject-out cross-validation (LOSOCV)" methods are reported as the final results. Unfortunately, we mistakenly attributed the accuracy of one method. While we correctly listed the name of the method used, which is KNN with ten-fold cross-validation, we inadvertently entered the precision of the KNN with leave-one-subject-out cross-validation (LOSOCV) method. Therefore, we updated our results table (Table 7). The correct Table 7 is shown below. (Table presented.) Comparison of the performance of the proposed method with existing methods Study Method Dataset Accuracy Sensitivity Specificity Shim et al. [38] SVM 34 SZ-34 healthy 88.24% – – Siuly et al. [7] EBT – EMD (Empirical Mode Decomposition) 49 SZ-32 healthy 89.59% 0.8976 0.8932 Phang et al. [39] Multi-domain connectome CNN 45 SZ-39 healthy 91.69% 0.9111 0.925 Jahmunah et al. [40] Non-linear features, decision-tree, neural network, SVM 14 SZ and 14 healthy 92.90% 0.9345 0.9224 Akbari et al. [41] Phase space dynamic & graphical features 14 SZ and 14 healthy 94.8% 0.943 0.952 Shalbaf et al. [20] ResNet-18-SVM 14 SZ and 14 healthy 94.84% 0.980 0.9239 Bagherzadeh et al. [42] DenseNet121 model 14 SZ and 14 healthy 96.26% 0.9548 0.9702 Sharma et al. [43] KNN with ten-fold 14 SZ and 14 healthy 99.21% 0.9942 0.9905 LOSOCV 97.20% 0.9649 0.9806 Proposed method CWT—Self distillation- VGG16 14 SZ and 14 healthy 97.81 0.970 0.986 The original article has been corrected. © Australasian College of Physical Scientists and Engineers in Medicine 2024.

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Multi-parameter ultrasound imaging for musculoskeletal tissues based on a physics informed generative adversarial network

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Chinese Physics B 2025年第4期34卷 442-455页

作者： Pengxin Wang Heyu Ma Tianyu Liu Chengcheng Liu Dan Li Dean Ta Institute of Biomedical Engineering&Technology Academy for Engineering and TechnologyFudan UniversityShanghai 200433China State Key Laboratory of Integrated Chips and Systems Fudan UniversityShanghai 201203China Department of Electronic Engineering School of Information Science and TechnologyFudan UniversityShanghai 200438China Department of Biomedical Engineering School of Information Science and TechnologyFudan UniversityShanghai 200438China

Full waveform inversion(FWI)has showed great potential in the detection of musculoskeletal ***,FWI is an ill-posed inverse problem and has a high requirement on the initial model during the imaging *** inaccurate initial model may lead to local minima in the inversion and unexpected imaging results caused by cycle-skipping *** learning methods have been applied in musculoskeletal imaging,but need a large amount of data for *** by work related to generative adversarial networks with physical informed constrain,we proposed a method named as bone ultrasound imaging with physics informed generative adversarial network(BUIPIGAN)to achieve unsupervised multi-parameter imaging for musculoskeletal tissues,focusing on speed of sound(SOS)and *** the in-silico experiments using a ring array transducer,conventional FWI methods and BUIPIGAN were employed for multiparameter imaging of two musculoskeletal tissue *** results were evaluated based on visual appearance,structural similarity index measure(SSIM),signal-to-noise ratio(SNR),and relative error(RE).For SOS imaging of the tibia–fibula model,the proposed BUIPIGAN achieved accurate SOS imaging with best *** specific quantitative metrics for SOS imaging were SSIM 0.9573,SNR 28.70 dB,and RE 5.78%.For the multi-parameter imaging of the tibia–fibula and human forearm,the BUIPIGAN successfully reconstructed SOS and density distributions with SSIM above 94%,SNR above 21 dB,and RE below 10%.The BUIPIGAN also showed robustness across various noise levels(i.e.,30 dB,10 dB).The results demonstrated that the proposed BUIPIGAN can achieve high-accuracy SOS and density imaging,proving its potential for applications in musculoskeletal ultrasound imaging.

关键词： ultrasound image physics informed generative adversarial network musculoskeletal imaging

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14-Bit SAR ADC with on-Chip Digital Bubble Sorting Calibration Technology

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Chinese Journal of electronics 2025年第1期34卷 125-136页

作者： Hua Fan Zhuorui Chen Tongrui Xu Franco Maloberti Qi Wei Quanyuan Feng School of Integrated Circuit Science and Engineering(Exemplary School of Microelectronics) University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chongqing Institute of Microelectronics Industry Technology University of Electronic Science and Technology of China Department of Electrical Computer and Biomedical Engineering University of Pavia Department of Precision Instrument Tsinghua University Institute of Microelectronics Southwest Jiaotong University

This article designs a 14-bit successive approximation register analog-to-digital converter（SAR ADC）.A novel digital bubble sorting calibration method is proposed and applied to eliminate the effect of capacitor mismatch on the linearity of the SAR ADC. To reduce the number of capacitors, a hybrid architecture of a high 8-bit binary-weighted capacitor array and a low 6-bit resistor array is adopted by the digital-to-analog（DAC）. The common-mode voltage VCM-based switching scheme is chosen to reduce the switching energy and area of the DAC. The time-domain comparator is employed to obtain lower power consumption. Sampling is performed through a gate voltage bootstrapped switch to reduce the nonlinear errors introduced when sampling the input signal. Moreover, the SAR logic and the whole calibration is totally implemented on-chip through digital integrated circuit（IC） tools such as design compiler, IC compiler, etc. Finally, a prototype is designed and implemented using 0.18 μm bipolar-complementary metal oxide semiconductor（CMOS）-double-diffused MOS 1.8 V CMOS technology. The measurement results show that the SAR ADC with on-chip bubble sorting calibration method achieves the signal-to-noise-and-distortion ratio of 69.75 dB and the spurious-free dynamic range of 83.77 dB.

关键词： Semiconductor device measurement Capacitors Linearity Switches Calibration System-on-chip Frequency measurement Time-domain analysis Sorting Signal to noise ratio

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On the Impact of Microbeamformers in 3-D High Frame Rate Ultrasound Imaging: A Simulation Study

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IEEE Transactions on biomedical engineering 2025年第6期72卷 1941-1950页

作者： Castrignano, Lorenzo Tortoli, Piero Matrone, Giulia Crocco, Marco Savoia, Alessandro Stuart Ramalli, Alessandro University of Florence Department of Information Engineering Florence50139 Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia27100 Italy Esaote S.p.A. Genova16152 Italy Roma Tre University Department of Industrial Electronic and Mechanical Engineering Roma00146 Italy

Background and objective: three-dimensional ultrasound imaging is based on two-dimensional (2-D) arrays controlled by application-specific integrated circuits, which implement the so-called microbeamformer (μB) to reduce the channel count. μBs are designed for line-by-line scan sequences based on focused beams (FBs), providing low frame rates. On the other hand, high frame rate (HFR) imaging techniques using defocused beams are increasingly attractive for reconstructing detailed tissue and blood motion information. Although an increasing number of researchers works with μBs integrated into 2-D probes, there is limited literature on the influence of the μB on image quality in HFR imaging applications. Methods: This simulation work evaluates the μB impact on multi-plane HFR transthoracic echocardiography by considering a μB connected to sub-arrays of different sizes (from 2×2 to 8×8 elements). Scan sequences based on the transmission of FBs, focused wide beams (FWB), and planar diverging waves (PDW) were tested. B-mode images were reconstructed and compared to the ideal case (no μB) to assess the image resolution and contrast loss due to the μB. Results: Compared to the ideal case, the μB presence can yield resolution and contrast deterioration up to 17.5% and -26 dB, respectively. Such a deterioration corresponds to the transmission of widest PDW, and can be partially recovered through the synthetic transmit beams technique. Conclusion and significance: A μB designed for FB applications can be used for HFR imaging, although with impaired imaging quality. Guidelines are provided to achieve a suitable trade-off among image quality, μB size, channel number and frame rate. © 1964-2012 IEEE.

关键词： Application specific integrated circuits

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Integrated Strategy to Mitigate Motion-Induced Artifacts During Seizures in Electrical Impedance Tomography

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IEEE Sensors Journal 2025年第9期25卷 15155-15166页

作者： Xu, Jiaming Yang, Jingrong Wu, Xinyu Dong, Xiuzhen Shi, Xuetao Yang, Fang Wang, Lei Institute of Medical Research Northwestern Polytechnical University Shaanxi Xi’an710072 China Fourth Military Medical University School of Biomedical Engineering Department of Medical Electronic Engineering Xi’an710032 China Xijing Hospital Department of Neurology Xi’an710032 China

Accurate identification of the epileptogenic zone (EZ) is essential for epilepsy patients to achieve successful surgical outcomes. Electrical impedance tomography (EIT) has the potential to enhance the precision of EZ localization. However, motion-induced image artifacts present a considerable challenge to accurate EIT imaging. This study aims to mitigate motion-induced artifacts in EIT. We propose an integrated strategy to address the artifacts stemming from two scenarios associated with the motion: electrode disconnection and position uncertainty. We employ Z-score and Pearson’s correlation coefficient (PCC) of original boundary voltage to identify the disconnected electrodes, a whale optimization algorithm (WOA) optimized backpropagation neural network (BPNN) to correct erroneous data induced by disconnected electrodes and particle swarm optimized variational mode decomposition (PSO-VMD) to suppress the motion interference induced by position uncertainty electrodes. The results show that both EIT voltage and original boundary voltage return to their normal level after motion-induced artifacts suppression, resulting in a marked enhancement in EIT imaging quality. The strategy’s efficacy is confirmed through both animal experiments and trials with healthy volunteers. The methodology presented in this article is adept at mitigating motion interference in brain EIT, improving the accuracy and reliability of EZ localization. It also helps to expand the clinical application scenarios of EIT, enabling the monitoring of other diseases that might produce motion-related interference. © 2001-2012 IEEE.

关键词： Image enhancement

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Fully Automated Bladder Volume Measurement System Using a T-Shaped Ultrasound Probe and Free-Hand Motion-Based 3D Reconstruction

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IEEE Access 2025年 13卷 80247-80257页

作者： Park, Daehyun Cho, Hyunwoo Song, Ilseob Jang, Jihun Yoo, Yangmo Sogang University Department of Electronic Engineering Seoul04107 Korea Republic of Sogang University Medical Solution Institute Seoul04107 Korea Republic of Sogang University Department of Biomedical Engineering Seoul04107 Korea Republic of

Accurate measurement of bladder volume is critical for the effective management of urinary diseases. Among the available modalities, ultrasound imaging is favored due to its cost-effectiveness, non-ionizing nature, and capability to provide real-time bladder volume measurements. To enhance convenience in clinical practice, ultrasound bladder scanners have been developed and adopted in various settings. However, wobbling probe devices present significant limitations, including their inability to provide real-time images, the requirement for pre-scan alignment to ensure precise probe positioning, and the necessity for multiple volume estimations to achieve measurement accuracy. Additionally, conventional image-based estimations utilizing standard ultrasound machines or recently developed bladder scanners face challenges related to the use of shape coefficients. These methods typically rely on two image planes, which fail to fully capture the bladder’s shape, resulting in considerable errors in volume estimation when compensating for shape with coefficients. To overcome these limitations, this study proposes a novel automated bladder volume measurement technique that employs a T-shaped cross-array ultrasound probe in conjunction with free-hand motion-based 3D reconstruction. The T-shaped cross-array enables the simultaneous acquisition of sagittal and transverse view images of the bladder. By moving the probe at the suprapubic region, continuously acquired sagittal images can be combined into a 3D volume, with motion compensation achieved via the transverse images. Consequently, the proposed method can accurately reconstruct the entire bladder and measure its volume without the need for shape correction coefficients, while also reducing errors associated with ultrasound probe positioning. The proposed methodology was validated through phantom experiments simulating both spherical and irregular bladder shapes. The results demonstrated an error rate ranging from -8.8% to

关键词： Ultrasonic imaging

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for Computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Dual-Branch Knowledge Fusion Network for Differential Diagnosis of Lung Cancer Cytopathology Images

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Journal of Shanghai Jiaotong University (Science) 2025年 1-11页

作者： Tang, Songqiao Geng, Chen Gong, Wei Hu, Fuyuan Dai, Yakang School of Electronic and Information Engineering Suzhou University of Science and Technology Jiangsu Suzhou215009 China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences Jiangsu Suzhou215163 China Department of Pathology Lishui Central Hospital Zhejiang Lishui323000 China

Adenocarcinoma and squamous cell carcinoma constitute a significant proportion of lung cancer cases, with distinct treatment strategies and prognoses. Accurate differentiation is crucial for clinical treatment. Advanced-stage lung cancer patients often cannot undergo tissue pathology, making cytopathology based on rapid on-site evaluation a feasible approach. However, this method faces challenges such as a shortage of digital pathology experts and diagnostic subjectivity. Current computer-aided digital cytopathology studies often overlook the spatial information between cells. This study proposes a knowledge-fusion cross-attention classification network, utilizing a graph convolutional network branch and a convolutional neural network branch to respectively introduce spatial information knowledge and texture knowledge. The knowledge is fused and classified using a multilayer perceptron, with a cross-attention mechanism proposed for node optimization during the pooling stage. Experiments on a pulmonary cell pathology dataset containing 209 cases showed that the proposed method achieved an accuracy of 0.925 in distinguishing adenocarcinoma from squamous cell carcinoma, with both recall and precision at 0.950, an F1-score of 0.920, and AUC of 0.980. This performance surpasses all comparison methods. In summary, the proposed method can improve the accuracy of diagnosing lung adenocarcinoma and squamous cell carcinoma, providing assistance in rapid intraoperative pathology for lung cancer patients. © Shanghai Jiao Tong University 2024.

关键词： Lung cancer

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