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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shen, Jiachen Wang, Wenxuan Chen, Chen Jiao, Jianbo Liu, Jing Zhang, Yan Song, Shanshan Li, Jiangyun School of Automation and Electrical Engineering University of Science and Technology Beijing China Center for Research in Computer Vision University of Central Florida United States School of Computer Science University of Birmingham United Kingdom Institute of Automation Chinese Academy of Sciences China

The "pre-training then fine-tuning (FT)" paradigm is widely adopted to boost the model performance of deep learning-based methods for medical volumetric segmentation. However, conventional full FT incurs high computational and memory costs. Thus, it is of increasing importance to fine-tune pre-trained models for medical volumetric segmentation tasks in a both effective and parameter-efficient manner. In this paper, we introduce a new framework named Med-Tuning to realize parameter-efficient tuning (PET) for medical volumetric segmentation task and an efficient plug-and-play module named Med-Adapter for task-specific feature extraction. With a small number of tuned parameters, our framework enhances the 2D baselines’s precision on segmentation tasks, which are pre-trained on natural images. Extensive experiments on three benchmark datasets (CT and MRI modalities) show that our method achieves better results than previous PET methods on volumetric segmentation tasks. Compared to full FT, Med-Tuning reduces the fine-tuned model parameters by up to 4×, with even better segmentation performance. Our project webpage is at https://***/Med-Tuning/. © 2024 CC-BY 4.0, J. Shen, W. Wang, C. Chen, J. Jiao, J. Liu, Y. Zhang, S. Song & J. Li.

关键词： Image enhancement

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Deepfake Detection in Cancer Medical Imaging Using CNN Architectures

Deepfake Detection in Cancer Medical Imaging Using CNN Arch...

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st International Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Alhalabi, Dima Talal Alawida, Moatsum Chikhaoui, Belkacem Hamadeh, Hala Samer Department of Computer Sciences Abu Dhabi University Abu Dhabi59911 United Arab Emirates Applied Artificial Intelligence Institute TELUQ University Montreal Canada

ISBN: (纸本)9789819614820

Deepfake technology poses an emerging threat in the medical field, as its potential to manipulate medical scans could result in misdiagnoses, fraudulent claims, and serious health risks. This study addresses the urgent need for robust detection methods to safeguard medical imaging systems and patient safety. We developed a deepfake detection model by evaluating four Convolutional Neural Network (CNN) architectures: VGG16, EfficientNetV2, InceptionV3, and Sequential. These models were assessed using Precision, Recall, F1-Score, and Accuracy metrics to determine their effectiveness. CT scan images were resized to 256×256 pixels, and the models were trained for 100 epochs with early stopping to enhance training efficiency. The EfficientNetV2 architecture initially achieved the highest accuracy of 92% without data augmentation. However, following data augmentation, both EfficientNetV2 and VGG16 reached an accuracy of 93%, with VGG16 being selected due to its superior recall rate of 98%, effectively minimizing false negatives and improving deepfake detection reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Localizable Instruments for Navigated Treatment of Ischemic Stroke

Localizable Instruments for Navigated Treatment of Ischemic ...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Stevanovic, Lena Bodanowitz, Ramona Mittmann, Benjamin J. Greiner-Perth, Ann-Kathrin Marschall, Eva Kannberg, Tobias Baumgärtner, Timo Braun, Michael Schmitz, Bernd Franz, Alfred M. Institute for Medical Engineering and Mechatronics Ulm University of Applied Sciences Germany Institute for Computer Science Ulm University of Applied Sciences Germany Neuroradiology Section Günzburg District Hospital Germany

ISBN: (纸本)9783658416560

Mechanical thrombectomy as a therapeutic option for ischemic stroke can possibly be supported by localizing the used catheters and guidewires during the intervention. In this study we equipped a probing catheter and a guidewire with electromagnetic (EM) sensors that can be localized in real time by means of an EM field generator. These instruments were tested regarding tracking accuracy and handling in laboratory and clinics. While accuracy was better than 0.4mm in all cases and the probing catheter showed promising handling and robustness, we observed handling problems and a sensor defect in case of the guidewire. We further demonstrated 3D navigation of the tracked catheter by probing 10 targets inside a vascular phantom with an average error of 1.8 mm. © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers

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Internet of Things and Cyber-Physical Systems 2023年第1期3卷 28-36页

作者： Nabavi, Sayyidshahab Wen, Linfeng Gill, Sukhpal Singh Xu, Minxian Institute of Electronic Engineering FH JOANNEUM University of Applied Sciences Graz Austria Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

Edge-Cloud Datacenters (ECDCs) have been massively exploited by the owners of technology and industrial centers to satisfy the user demand. At the same time, the amount of energy used by these data centers is considerable. To address this challenge, Virtual Machine (VM) placement of the ECDCs plays an important role;therefore, assigning VM properly to physical machines (PM) can significantly decrease the amount of energy consumption. The applied assigning technique simultaneously must consider additional objectives involving traffic and power usage of the network elements, which makes it a challenging problem. This paper proposes a multi-objective VM placement approach in edge-cloud data centers, which uses Seagull optimization to optimize power and network traffic together. In this strategy, the network traffic among PMs is reduced by concentrating the communications of VMs on the same PMs to reduce the amount of transferred data through the network and reduce the PMs’ power consumption by consolidating VMs to fewer PMs, which consumes less energy. We evaluate with simulations in CloudSim and test two different network topologies, VL2 (Virtual Layer 2) and three-tier, to validate that the proposed approach can effectively reduce traffic and power consumption in ECDCs. The experimental results show that our proposed method can decrease energy consumption by 5.5% while simultaneously reducing network traffic by 70% and the power consumption of the network components by 80%. © 2023 The Author(s)

关键词： Electric power utilization

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LoRaWAN, NB-IoT and other radio networks for agricultural applications 26

LoRaWAN, NB-IoT and other radio networks for agricultural ap...

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26. ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 26th ITG Symposium on Mobile Communication: Technologies and Applications

作者： Westerkamp, Clemens Grunwald, Alexander Schaarschmidt, Marco Institute of Computer Engineering University of Applied Sciences Osnabrueck Osnabrueck Germany

ISBN: (纸本)9783800758739

Agricultural applications often operate in regions, where public radio network infrastructure is limited. A network comparison for two typical use cases shows that different agricultural applications can be conducted even if the public network coverage is limited. © 2022 Mobilkommunikation: Technologien und Anwendungen - 26. ITG-Fachtagung

关键词： Agriculture

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Vocalics in Human-Drone Interaction

Vocalics in Human-Drone Interaction

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Marc Lieser Ulrich Schwanecke Computer Vision & Mixed Reality Group of RheinMain University of Applied Sciences Wiesbaden Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

As the presence of flying robots continues to grow in both commercial and private sectors, it necessitates an understanding of appropriate methods for nonverbal interaction with humans. While visual cues, such as gestures incorporated into trajectories, are more apparent and thoroughly researched, acoustic cues have remained unexplored, despite their potential to enhance human-drone interaction. Given that additional audiovisual and sensory equipment is not always desired or practicable, and flight noise often masks potential acoustic communication in rotary-wing drones, such as through a loudspeaker, the rotors themselves offer potential for nonverbal communication. In this paper, the consequential sound during the flight of a quadrotor is utilized and modified to carry acoustic information while maintaining the visually perceived flight characteristics. A user study (N=192) demonstrates that acoustically augmenting the trajectories of two aerial gestures with the proposed approach makes them more easily distinguishable. This enhancement contributes to human-drone interaction through onboard means, particularly in situations where the human cannot see or look at the drone.

关键词： Loudspeakers Visualization Acoustic communication (telecommunication) Noise Rotors Robot sensing systems Acoustics Trajectory Drones Quadrotors

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Graph-Based Deep Learning on the Swiss River Network 13th

Graph-Based Deep Learning on the Swiss River Network

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13th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2023

作者： Fankhauser, Benjamin Bigler, Vidushi Riesen, Kaspar Institute of Computer Science University of Bern Bern Switzerland Institute for Optimisation and Data Analysis Bern University of Applied Sciences Biel Switzerland

ISBN: (纸本)9783031427947

Major European rivers have their sources in the Swiss Alps. Data from these rivers and their tributaries have been collected for decades with consistent quality. We use GIS data to extract the structure of each river and link this structure to 81 river water stations (that measure both water temperature and discharge). Since the water temperature of a river is strongly dependent on the air temperature, we also include 44 weather stations (which measure, for instance, air or soil temperature). Based on this large data corpus, we present in this paper a novel graph representing the water network of Switzerland. Our goal is to accelerate the research of the complex relationships at the (Swiss) water bodies. In particular, we present different graph-based pattern recognition tasks that can be solved on the novel water body graph. In a first evaluation, we use graph-based methods to solve two of these tasks, outperforming current state-of-the-art systems by several percentage points. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Water temperature

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Using the 360-Degree Method to Evaluate Overall Performance and its Impact on the Overall Quality of University Education

Using the 360-Degree Method to Evaluate Overall Performance ...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Khalifa, Mahmoud Al-Baz, Mohamed Mostafa Eltahir, Ahmed Essam College of Administrative Sciences Applied Science University Manama Bahrain Suez Canal University Faculty of Commerce Ismailia Egypt Higher Institute of Commercial and Computer Sciences Ismailia Egypt

ISBN: (纸本)9798350369106

The study aimed to identify the use of the 360-degree method to evaluate overall performance (self-evaluation, downward evaluation, upward evaluation, horizontal evaluation) and its impact on the overall quality of university education resources in Egyptian universities. The study sample consisted of (357) individual faculty members, and (342) (a single member of the administrative body in Egyptian universities. The study relied on the descriptive analytical approach, and used two survey lists to identify the opinions of the study sample. The results of the study reached a statistically significant positive relationship at the level of (0.01) between the dimensions of using the 360 degree method to evaluate overall performance and the overall quality of university education in Egyptian universities there is a statistically significant positive impact at the level of (0.01) for the following dimensions of using the 360 degree method to evaluate overall performance: (downward evaluation, horizontal evaluation, self-evaluation) on the overall quality of university education in Egyptian universities and the absence of a statistically significant impact for the upward evaluation dimension on the overall quality of university education in Egyptian universities. The study recommended paying attention to the strong positive relationship linking the dimensions of using the 360 degree method to evaluate overall performance and the overall quality of university education, and The necessity of paying attention to the overall quality of university education by making optimal use of the resources available in universities, building and strengthening human and social relations, and establishing an accurate information system for managing overall quality, and The necessity for Egyptian universities to rely heavily on the use of the 360-degree method to evaluate overall performance, and to move away from traditional and routine methods. © 2024 IEEE.

关键词： Information use

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Advancing Industrial Cybersecurity: Machine Learning-Based Detection and Mitigation of IIoT Attacks 5

Advancing Industrial Cybersecurity: Machine Learning-Based D...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kumar, S Gouri Kiran Prasad, Raavi Satya College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman Dhanekula Institute of Engineering & Technology Department of Computer Science & Engineering A.P. Vijayawada India

ISBN: (纸本)9798331518097

A threatening entry or exploitation of vulnerabilities within an industrial network or system connected to IoT devices is an IIoT (Industrial Internet of Things) attack. Due to manufacturing delays, broken equipment, lost confidential data, and fines from the government, cyberattacks on IIoT systems can cause significant financial losses. Rapid reaction and recovery are made possible by efficient detection systems, which can lessen these losses. Many researchers have applied machine learning (ML) approaches to predict IIoT attacks in advance. Industrial networks can be highly effectively and dynamically protected against various cyber threats by utilizing MLthrough IIoT attack distinguishing. This ML methodology is a vital component of today's cybersecurity toolkit for IIoT contexts owing to its capacity for handling massive info, adjusting for novel threats, & delivering insights in real-time. Industrial networks can be highly effectively and dynamically protected against cyber threats by utilizing ML to IIoT attack identification. ML is a vital component of today's cybersecurity toolkit for IIoT contexts for its capacity to handle massive quantities of info, adjust to emerging risks, & deliver insights in real time. IIoT data can contain noise, missing values, or irrelevant information, negatively affecting model performance. Identifying and extracting relevant features from IIoT data was highly complex and time-consuming. The methodology's effectiveness is heavily related to features with high quality utilized. This paper discusses the various techniques, their limitations, and the gaps studied during the literature survey. Among the machine learning approaches, the model should adapt to the new infrastructure attacks, which is effectively possible only through meta-heuristic learning algorithms. © 2024 IEEE.

关键词： Cyber attacks

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