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Faster R-CNN in Healthcare and Disease Detection: A Comprehensive Review

Faster R-CNN in Healthcare and Disease Detection: A Comprehe...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Tian, Jiawei Lee, Seungho Kang, Kyungtae Major in Bio Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Ansan-si Korea Republic of Hanyang University Dept. of Applied Artificial Intelligence Ansan-si Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan-si Korea Republic of

ISBN: (纸本)9798331510756

This review examines the applications, challenges, and prospects of Faster Region-based Convolutional Neural Networks (Faster R-CNN) in healthcare and disease detection. Through a meta-analysis of Web of science literature from 2017 to 2024, we provide insights into the evolving landscape of Faster R-CNN in medical contexts. The algorithm can be applied to medical image analysis across various modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound, microscopy, and endoscopy. Its applications extend to optical RGB images for dermatological and surgical uses, as well as broader healthcare areas such as posture detection, medication recognition, and assistive technologies. Despite its success, Faster R-CNN faces challenges in handling subtle abnormalities, addressing class imbalance in medical datasets, ensuring result interpretability, and managing patient privacy. Our analysis reveals dominant application areas, technological advancements, and integration trends with other artificial intelligence technologies. The review highlights Faster R-CNN's significant impact on improving diagnostic accuracy and healthcare delivery while acknowledging the need for continued research to address limitations. Emerging directions include real-time disease detection and advancements in personalized medicine, underscoring Faster R-CNN's potential to further transform healthcare practices and patient outcomes. © 2025 IEEE.

关键词： computerized tomography

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Zoom-DF: A Dataset for Video Conferencing Deepfake 1

Zoom-DF: A Dataset for Video Conferencing Deepfake

引用

1st ACM Workshop on Security Implications of Deepfakes and Cheapfakes, WDC 2022, co-located with ACM AsiaCCS 2022

作者： Park, Geon-Woo Park, Eun-Ju Woo, Simon S. Computer Science & Engineering Dept. Sunkyunkwan University Suwon Korea Republic of Applied Data Science Dept. Sunkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9781450391788

With the rapid growth of deep learning methods, AI technologies for generating deepfake videos also have been significantly advanced. Nowadays, the manipulated videos such as deepfakes are so sophisticated that one cannot easily differentiate between real and fake, and one can create such videos with little effort. However, such technologies can be likely to be abused by people with malicious intents. To address this issue, approaches and efforts to detect deepfakes have been researched significantly. However, the performances of the detectors in general depends on the quantity and quality of the training data. In this paper, we introduce a new deepfake dataset, Zoom-DF, which can be injected during the remote meeting and video conferencing, to create a sequence of fake participant images. While most deepfake datasets focus on the face area, our dataset primarily targets for the remote meeting, and manipulates movements of the participants. We evaluate existing deepfake detectors on our new Zoom-DF dataset and present the performance results. © 2022 ACM.

关键词： Video conferencing

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CLEVA-COMPASS: A CONTINUAL LEARNING EVALUATION ASSESSMENT COMPASS TO PROMOTE RESEARCH TRANSPARENCY AND COMPARABILITY 10

CLEVA-COMPASS: A CONTINUAL LEARNING EVALUATION ASSESSMENT CO...

引用

10th International Conference on Learning Representations, ICLR 2022

作者： Mundt, Martin Lang, Steven Delfosse, Quentin Kersting, Kristian AI and Machine Learning Group Dept. of Computer Science TU Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Darmstadt Germany

What is the state of the art in continual machine learning? Although a natural question for predominant static benchmarks, the notion to train systems in a lifelong manner entails a plethora of additional challenges with respect to set-up and evaluation. The latter have recently sparked a growing amount of critiques on prominent algorithm-centric perspectives and evaluation protocols being too narrow, resulting in several attempts at constructing guidelines in favor of specific desiderata or arguing against the validity of prevalent assumptions. In this work, we depart from this mindset and argue that the goal of a precise formulation of desiderata is an ill-posed one, as diverse applications may always warrant distinct scenarios. Instead, we introduce the Continual Learning EValuation Assessment Compass: the CLEVA-Compass. The compass provides the visual means to both identify how approaches are practically reported and how works can simultaneously be contextualized in the broader literature landscape. In addition to promoting compact specification in the spirit of recent replication trends, it thus provides an intuitive chart to understand the priorities of individual systems, where they resemble each other, and what elements are missing towards a fair comparison. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

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Amortized Variational Peak Fitting For Spectroscopic Data 33

Amortized Variational Peak Fitting For Spectroscopic Data

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33rd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2023

作者： Hansen, David Frich Alstrom, Tommy Sonne Schmidt, Mikkel N. Technical University of Denmark Richard Petersens Dept. of Applied Mathematics and Computer Science Plads 324 Kgs. Lyngby2800 Denmark

ISBN: (纸本)9798350324112

Spectroscopic analysis relies on identifying and understanding the spectral peaks that represent unique characteristics of an analyte. In high-speed real-time settings, current peak fitting techniques, particularly Bayesian methods involving MCMC or variational approximation, can be prohibitively expensive. We propose an unsupervised method using a convolutional neural network to estimate the number of peaks and their parameters along with posterior uncertainty, by amortizing variational inference in a classical parametric peak model. In a simulated data study, our method reliably determines the number of peaks, precisely estimates parameters similar to direct variational inference, and accurately captures uncertainty comparable to MCMC methods. Our novel, fast, and precise method for Bayesian spectral analysis opens new possibilities in real-time spectral data processing for high-speed monitoring and control. © 2023 IEEE.

关键词： Spectrum analysis

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Model and Procedure for Assessing the Qualification of a Software Developer 23

Model and Procedure for Assessing the Qualification of a Sof...

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23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Gavriliev, Erchimen I. Avdeenko, Tatiana V. Novosibirsk State Technical University Dept. of Theoretical and Applied Computer Science Novosibirsk Russia

ISBN: (纸本)9781665498043

Developers' qualifications and their professional development are the important aspects of IT project's success. Software developers are interested in advancing their careers and improving their skills in order to maintain their level of professional value both for organizations and for the labor market. However, the evaluation of developers' qualifications is frequently based on the subjective opinion of managers, which can negatively affect the further professional development of employees. Quantitative metrics become particularly relevant in the assessment, because they can reduce the level of subjectivity. Therefore, the objective of this work is to develop data model for assessing the qualifications of software developers. The proposed model is based on data from version control system, knowledge management system and task management system. In order to collect information from these systems, as well as to carry out calculations, a decision support system based on the *** software platform was developed. The designed model and system were implemented in a software development company. For the assessment of relations between selected metrics and developers' qualifications a multiple regression analysis was carried, where the employee's qualification rating assessed by the manager was the dependent variable. As a result, 2 models were prepared to assess the responsibility and the level of technical skills. Together, these models will provide a more comprehensive picture of developer's qualification. © 2022 IEEE.

关键词： Managers

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Contextual Decision-Making with Knapsacks Beyond the Worst Case 38

Contextual Decision-Making with Knapsacks Beyond the Worst C...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Zhaohua Ai, Rui Yang, Mingwei Pan, Yuqi Wang, Chang Deng, Xiaotie School of Computer Science Peking University Haidian Beijing China IDSS LIDS Massachusetts Institute of Technology CambridgeMA02139 United States Dept. of Management Science and Engineering Stanford University StanfordCA94305 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Dept. of Computer Science Northwestern University EvanstonIL60208 United States School of Computer Science Institute for Artificial Intelligence Peking University Haidian Beijing China

We study the framework of a dynamic decision-making scenario with resource constraints. In this framework, an agent, whose target is to maximize the total reward under the initial inventory, selects an action in each round upon observing a random request, leading to a reward and resource consumptions that are further associated with an unknown random external factor. While previous research has already established an Oe(√T) worst-case regret for this problem, this work offers two results that go beyond the worst-case perspective: one for the worst-case gap between benchmarks and another for logarithmic regret rates. We first show that an Ω(√T) distance between the commonly used fluid benchmark and the online optimum is unavoidable when the former has a degenerate optimal solution. On the algorithmic side, we merge the re-solving heuristic with distribution estimation skills and propose an algorithm that achieves an Oe(1) regret as long as the fluid LP has a unique and non-degenerate solution. Furthermore, we prove that our algorithm maintains a near-optimal Oe(√T) regret even in the worst cases and extend these results to the setting where the request and external factor are continuous. Regarding information structure, our regret results are obtained under two feedback models, respectively, where the algorithm accesses the external factor at the end of each round and at the end of a round only when a non-null action is executed. © 2024 Neural information processing systems foundation. All rights reserved.

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Exploratory Evaluation of Speech Content Masking 15

Exploratory Evaluation of Speech Content Masking

引用

15th ITG Conference on Speech Communication

作者： Williams, Jennifer Pizzi, Karla Noé, Paul-Gauthier Das, Sneha Electronics and Computer Science University of Southampton United Kingdom Fraunhofer AISEC Garching Germany Technical University Munich Germany University of Avignon France Dept. of Applied Mathematics and Computer Science Technical University of Denmark Denmark

ISBN: (纸本)9783800761654

Most recent speech privacy efforts have focused on anonymizing acoustic speaker attributes but there has not been as much research into protecting information from speech content. We introduce a toy problem that explores an emerging type of privacy called "content masking" which conceals selected words and phrases in speech. In our efforts to define this problem space, we evaluate an introductory baseline masking technique based on modifying sequences of discrete phone representations (phone codes) produced from a pre-trained vector-quantized variational autoencoder (VQ-VAE) and re-synthesized using WaveRNN. We investigate three different masking locations and three types of masking strategies: noise substitution, word deletion, and phone sequence reversal. Our work attempts to characterize how masking affects two downstream tasks: automatic speech recognition (ASR) and automatic speaker verification (ASV). We observe how the different masks types and locations impact these downstream tasks and discuss how these issues may influence privacy goals. © VDE VERLAG GMBH Berlin Offenbach.

关键词： Toys

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Optimizing Healthcare Accessibility and Effectiveness through the Implementation of AI Solutions for Remote Patient Monitoring 3

Optimizing Healthcare Accessibility and Effectiveness throug...

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3rd IEEE World Conference on applied Intelligence and Computing, AIC 2024

作者： Singh, Jagendra Sahu, Dinesh Prasad Kumar, Ashwini Ahmad, Salman Khursheed Majumder, Jayeeta Gangopadhyay, Sourav Bennett University School of Computer Science Engineering & Technology Greater Noida India Camellia Institute of Technology & Management Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology & Management Dept. of Applied Computational Science and Engineering Greater Noida India Brainware University Department of Hospital Management Kolkata India

ISBN: (纸本)9798350384598

This study investigates the optimization of healthcare accessibility and effectiveness by integrating artificial intelligence (AI) solutions within Internet of Things (IoT) systems for remote patient monitoring. Embracing a holistic approach, we deploy medical sensors, including temperature, pressure, heart rate, and oxygen sensors, to collect patients' continuous health metrics (vital signs). These sensed data are then transmitted to an Arduino controller, responsible for managing data communication with the cloud infrastructure using a Wi-Fi module that facilitates data storage and analysis. Stored data are accessed by authorized healthcare professionals, who exploit machine learning models, including Artificial Neural Networks (ANN), Logistic Regression (LR) and Decision Tree (DT), to forecast patients' health outcome in advance using the sensed data. A rigorous evaluation demonstrated the IoT system's effectiveness, based on testing with sensed data from ten patients over a month, where 70% of the data were used for training the models, and the rest for testing. The proposed IoT system demonstrates the success;ANN exhibited the highest accuracy at 97.88%, followed by LR at 93.43%, and DT at 91.20%. Precision, recall, and F1 scores further validated the models' ability to predict health outcome accurately. Evaluation of the accuracy and loss curves showed the convergence of the models towards optimal performance. This study contributes to remote healthcare monitoring and provides a promising IoT paradigm for realizing ubiquitous remote monitoring, optimized healthcare effectiveness, and patient outcome through AI-enabled IoT systems. © 2024 IEEE.

关键词： Remote patient monitoring

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A Framework for the Systematic Assessment of Anomaly Detectors in Time-Sensitive Automotive Networks

A Framework for the Systematic Assessment of Anomaly Detecto...

引用

IEEE Vehicular Networking Conference (VNC)

作者： Philipp Meyer Timo Häckel Teresa Lübeck Franz Korf Thomas C. Schmidt Dept. Computer Science Hamburg University of Applied Sciences Germany

ISBN: (数字)9798350362701

ISBN: (纸本)9798350362718

Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard Time-Sensitive Networks (TSNs) require monitoring for safety and - as versatile platforms to host Network Anomaly Detection Systems (NADSs) - for security. Still a thorough evaluation of anomaly detection methods in the context of hard real-time operations, automotive protocol stacks, and domain specific attack vectors is missing along with appropriate input datasets. In this paper, we present an assessment framework that allows for reproducible, comparable, and rapid evaluation of detection algorithms. It is based on a simulation toolchain, which contributes configurable topologies, traffic streams, anomalies, attacks, and detectors. We demonstrate the assessment of NADSs in a comprehensive in-vehicular network with its communication flows, on which we model traffic anomalies. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types. Our approach translates to other real-time Ethernet domains, such as industrial facilities, airplanes, and UAVs.

关键词： Systematics Protocols Detectors Real-time systems Vectors Topology Security

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Exploring the Effect of Age and Sex on Subject-Independent EEG-Based Emotion Recognition Methods

Exploring the Effect of Age and Sex on Subject-Independent E...

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Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Camilo E. Valderrama Anshul Sheoran Qian Liu Dept. of Applied Computer Science University of Winnipeg Winnipeg Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

In the field of emotion recognition, there are two approaches used to develop predictive models that recognize emotions from EEG signals: subject-dependent and subject-independent. Subject-independent models, although more practical, tend to yield a lower performance due to the high variability of EEG signals between individuals. Recent studies have indicated that incorporating prior demographic information about individuals can improve the accuracy of subject-independent approaches. Some studies have supported this claim by showing that including individuals’ sex can boost model accuracy. However, until now, no one has used interpretable models to measure to what extent demographics can enhance subject-independent approaches. In this work, we follow this direction by using a logistic regression model to correlate the output of a deep learning model with subjects’ age and sex, thereby evaluating whether these factors impact emotion prediction. Our analysis indicates that the ‘sex’ variable significantly influenced the predictions of the deep learning model in three out of five emotions, whereas ‘age’ does not have any effect. These findings suggest that sex is a factor that needs to be considered when designing EEG-based emotion recognition models, which could lead to more robust subject-independent models with potential applications in areas such as healthcare, education, and marketing.

关键词： Deep learning Emotion recognition Logistic regression Accuracy Stacking Education Medical services

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