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arXiv 2024年

作者： Sohail, Shahab Saquib Himeur, Yassine Kheddar, Hamza Amira, Abbes Fadli, Fodil Atalla, Shadi Copiaco, Abigail Mansoor, Wathiq School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates LSEA Laboratory Department of Electrical Engineering University of Medea 26000 Algeria Department of Computer Science University of Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL), which decreases dependency and costs by utilizing knowledge gained from a source data/task in training a target data/task, has been widely investigated. Numerous DTL frameworks have been suggested for aligning point clouds obtained from several scans of the same scene. Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data’s quality by dealing with noise and missing points. Ultimately, fine-tuning and DA approaches have demonstrated their effectiveness in addressing the distinct difficulties inherent in point cloud data. This paper presents the first review shedding light on this aspect. it provides a comprehensive overview of the latest techniques for understanding 3DPC using DTL and domain adaptation (DA). Accordingly, DTL’s background is first presented along with the datasets and evaluation metrics. A well-defined taxonomy is introduced, and detailed comparisons are presented, considering different aspects such as different knowledge transfer strategies, and performance. The paper covers various applications, such as 3DPC object detection, semantic labeling, segmentation, classification, registration, downsampling/upsampling, and denoising. Furthermore, the article discusses the advantages and limitations of the presented frameworks, identifies open challenges, and suggests potential research directions. © 2024, CC BY.

关键词： Object detection

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SAM+nnUNet: Deep-learning-based Head and Neck Tumor Segmentation on FDG PET

SAM+nnUNet: Deep-learning-based Head and Neck Tumor Segmenta...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Z. Fang Z. Lu H. Liu Y. Liu G. S.P. Mok Department of Electrical and Computer Engineering Faculty of Science and Technology Biomedical Imaging Laboratory (BIG) University of Macau Macau China Department of Nuclear Medicine The Affiliated Hospital Southwest Medical University Luzhou China Center for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Macau China

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

FDG PET is a useful tool for diagnosis, staging and treatment monitoring on Head and Neck (H&N) tumors. Segmentation is usually performed for the tumors while the deep learning technique is emerging as an efficient yet precise alternate to manual segmentation. The performance of the conventional CNN-based segmentation networks is limited by insufficient training data. The Segment Anything Model (SAM) has been released by Meta, initiating a paradigm shift to a “smart” interactive segmentation that provides competitive performance when properly prompted. In this study, we proposed a SAM+nnUNet for H&N tumor segmentation on FDG PET to enhance the original SAM, in which the prompting process can be time-consuming and require a high degree of expertise. The HECKTOR2022 PET/CT dataset was used, with 420 patient images for training, 50 cases for validation, and 54 cases for testing. The training dataset of PET was used to fine-tune the mask decoder of pre-trained SAM, while both PET and CT were used to train an nnUNet. The trained nnUNet was first used to perform a preliminary segmentation of the tumors on the testing dataset, which was used to generate bounding box prompts for the SAM input after visual inspection and manual adjustment if necessary. Thresholding segmentation based on 42% of maximum intensity and various U-Net variants are implemented for comparison. Dice Similarity Coefficient (DSC), Hausdorff Distance at 95th percentile (HD95), and SUVmean of segmented tumors compared to the ground truth were analyzed. Our results indicate that 5 out of 54 cases need manual adjustment for the nnUNet-based prompts for SAM. SAM+nnUNet performs best in all quantitative indices. We conclude that our proposed method is promising for H&N tumor segmentation on PET.

关键词： Training Image segmentation Visualization Head Training data Manuals Artificial neural networks

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Telehealth Data Security and Privacy Solutions for Sensitive Health Records using Cloud computing and Isolation Forest Algorithm

Telehealth Data Security and Privacy Solutions for Sensitive...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： A Prathik S. Parveen Banu B S Sagar A. John Felix Prakash R Thamizhamuthu S. Velmurugan Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Electrical and Electronics Engineering REVA University Bengaluru Karnataka India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus-Chengalpattu Chennai Tamil Nadu India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Protecting the confidentiality of patient’s personal health information as it is sent and kept in the cloud is of the utmost importance due to the increasing number of telehealth services. This research provides a new healthcare data security approach using cloud computing and the Isolation Forest method. To identify important challenges and provide a novel technique, a thorough literature study on telehealth security, privacy issues, and cloud-based solutions. It uses the Isolation Forest method for anomaly detection in combination with encryption and access restriction systems to identify intrusion attempts. It uses simulated telehealth data in trials to test the system’s work. Findings show substantial gains in privacy and data security with computational burden. Healthcare practitioners and legislators will find results useful in their pursuit of solutions to the problem of securing patients’ private health information throughout the rise of telemedicine.

关键词： Cloud computing Data privacy Privacy Telemedicine Data security medical services Forestry Encryption Security Anomaly detection

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Two-Stream Regression Network for Dental Implant Position Prediction

arXiv

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arXiv 2023年

作者： Yang, Xinquan Li, Xuguang Li, Xuechen Chen, Wenting Shen, Linlin Li, Xin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong

In implant prosthesis treatment, the design of surgical guide requires lots of manual labors and is prone to subjective variations. When deep learning based methods has started to be applied to address this problem, the space between teeth are various and some of them might present similar texture characteristic with the actual implant region. Both problems make a big challenge for the implant position prediction. In this paper, we develop a two-stream implant position regression framework (TSIPR), which consists of an implant region detector (IRD) and a multi-scale patch embedding regression network (MSPENet), to address this issue. For the training of IRD, we extend the original annotation to provide additional supervisory information, which contains much more rich characteristic and do not introduce extra labeling costs. A multi-scale patch embedding module is designed for the MSPENet to adaptively extract features from the images with various tooth spacing. The global-local feature interaction block is designed to build the encoder of MSPENet, which combines the transformer and convolution for enriched feature representation. During inference, the RoI mask extracted from the IRD is used to refine the prediction results of the MSPENet. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TSIPR achieves superior performance than existing methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Optimization of Image Transmission in Semantic Communication Networks

Optimization of Image Transmission in Semantic Communication...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Wenjing Zhang Yining Wang Mingzhe Chen Tao Luo Dusit Niyato Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and ComputingUniversity of Miami Coral Gables FL USA School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781665435413

In this paper, a semantic communication framework for image transmission is investigated. In the framework, a server transmits image data to a set of users utilizing semantic communication techniques, which enable the server to transmit only the semantic information that accurately captures the meaning of an image. To evaluate the performance of the studied semantic communication system, we propose a multimodal metric called image-to-graph semantic similarity (ISS). The significance of this new metric is that it can measure the correlation of the meaning between semantic information and the original image. To meet the ISS requirement of each user, the server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. We formulate this problem as an optimization problem whose goal is to minimize the average transmission latency while reaching the ISS requirement. To solve this problem, we propose a model-based actor critic deep reinforcement learning (DRL) algorithm. Compared to traditional actor critic DRL, in the proposed algorithm, we design a novel value function to improve the action exploration thus improving the probability of finding an optimal solution. Simulation results show that the proposed method can reduce the transmission delay by 16.4% and improves the convergence speed by up to 50% compared to the traditional actor critic DRL.

关键词： Measurement Wireless communication Image communication Simulation Semantics Reinforcement learning Servers

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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Data science and engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Dimitrios I. Zaridis Eugenia Mylona Nikolaos Tachos Charalampos Kalantzopoulos Vasileios C. Pezoulas Dimitrios D. Koutsouris George K. Matsopoulos Kostas Marias Manolis Tsiknakis Dimitrios I. Fotiadis Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens GR15773 Athens Greece Biomedical Research Institute FORTH Ioannina Greece University of Ioannina Ioannina Greece Institute of Computer Science FORTH Heraklion Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece FORTH-IMBB Ioannina Greece Materials Science and Engineering Department Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina GR 45110 Greece

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland’s complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model’s performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net’s deficiency leading to the development of more specialized models in respect to these factors.

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Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations

arXiv

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arXiv 2023年

作者： Jiang, Zhongliang Bi, Yuan Zhou, Mingchuan Hu, Ying Burke, Michael Navab, Nassir The Chair for Computer Aided Medical Procedures and Augmented Reality Technical University of Munich Germany Shenzhen Institute of Advanced Technology Chinese Academy of Science China The Department of Electrical and Computer Systems Engineering Monash University Australia The Laboratory for Computer Aided Medical Procedures Johns Hopkins University BaltimoreMD21218 United States

Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal organs due to the advantages of being real-time and radiation-free. However, due to inter-operator variations, resulting images highly depend on the experience of sonographers. This work proposes an intelligent robotic sonographer to autonomously "explore"target anatomies and navigate a US probe to a relevant 2D plane by learning from the expert. The underlying high-level physiological knowledge from experts is inferred by a neural reward function, using a ranked pairwise image comparisons approach in a self-supervised fashion. This process can be referred to as understanding the "language of sonography". Considering the generalization capability to overcome inter-patient variations, mutual information is estimated by a network to explicitly disentangle the task-related and domain features in latent space. The robotic localization is carried out in coarse-to-fine mode based on the predicted reward associated with B-mode images. To validate the effectiveness of the proposed reward inference network, representative experiments were performed on vascular phantoms ("line"target), two types of ex-vivo animal organs (chicken heart and lamb kidney) phantoms ("point"target) and in-vivo human carotids, respectively. To further validate the performance of the autonomous acquisition framework, physical robotic acquisitions were performed on three phantoms (vascular, chicken heart, and lamb kidney). The results demonstrated that the proposed advanced framework can robustly work on a variety of seen and unseen phantoms as well as in-vivo human carotid data. Code: https://***/yuan-12138/MI-GPSR. Copyright © 2023, The Authors. All rights reserved.

关键词： Phantoms

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Design and exploration of vertically stacked complementary tunneling field-effect transistors

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Applied Physics Express 2024年第1期17卷 014001-014001页

作者： Thoti, Narasimhulu Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

The purpose of this letter is to study the design and explore vertically stacked complementary tunneling field-effect transistors (CTFETs) using CFET technology for emerging technology nodes. As a prior work, the CTFET’s device-level simulations are implemented and deliberated in strict compliance with the experimental settings. This work comprises the study of physical and DC analyses by scaling the p- to n-CTFET separation (D pn), being a significant factor in CFET/CTFET design for its process difficulty. By utilizing the 50% benefit in footprint, the work is further extended to CTFET static random access memory implementation and characterization with hold/read noise margin analysis. © 2023 The Author(s). Published on behalf of The Japan Society of Applied Physics by IOP Publishing Ltd

关键词： Si-Ge alloys

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Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach

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IEEE Transactions on Vehicular Technology 2025年

作者： He, Changpeng Lu, Yang Chen, Wei Ai, Bo Wong, Kai-Kit Niyato, Dusit State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China University College London Department of Electronic and Electrical Engineering LondonWC1E 6BT United Kingdom Yonsei University Yonsei Frontier Laboratory School of Integrated Technology Seoul03722 Korea Republic of Nanyang Technological University College of Computing and Data Science 639798 Singapore

An emerging fluid antenna system (FAS) brings a new dimension, i.e., the antenna positions, to deal with the deep fading, but simultaneously introduces challenges related to the transmit design. This paper proposes an "unsupervised learning to optimize" paradigm to optimize the FAS. Particularly, we formulate the sum-rate and energy efficiency (EE) maximization problems for a multiple-user multiple-input single-output (MU-MISO) FAS and solved by a two-stage graph neural network (GNN) where the first stage and the second stage are for the inference of antenna positions and beamforming vectors, respectively. The outputs of the two stages are jointly input into an unsupervised loss function to train the two-stage GNN. The numerical results demonstrate that the advantages of the FAS for performance improvement and the two-stage GNN for real-time and scalable optimization. Besides, the two stages can function separately. © 1967-2012 IEEE.

关键词： Graph neural networks

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