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Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study

BMC Medical Imaging 2024年第1期24卷 92页

作者： Chang, Joseph Lin, Bo-Ru Wang, Ti-Hao Chen, Chung-Ming Department of Biomedical Engineering College of Medicine and College of Engineering National Taiwan University No. 1 Sec. 1 Jen-Ai Road Taipei 100 Taipei 100 Taiwan EverFortune.AI Co. Ltd Taichung Taiwan The Data Science Degree Program College of Electrical Engineering and Computer Science National Taiwan University and Academia Sinica Taipei Taiwan Department of Radiation Oncology China Medical University Hospital Taichung Taiwan Department of Medicine China Medical University Taichung Taiwan

Background: The study aimed to develop and validate a deep learning-based computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. Methods: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. Results: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong’s CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. Conclusion: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings. © The Author(s) 2024.

关键词： Active learning Chest radiographs Deep learning Pleural effusion X-rays

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Fusion of Mixture of Experts and Generative Artificial Intelligence in Mobile Edge Metaverse

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Mao, Shiwen Kim, Dong In The College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Auburn University Auburn United States The School of Electrical and Computer Engineering University of Sydney Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

In the digital transformation era, Metaverse offers a fusion of virtual reality (VR), augmented reality (AR), and web technologies to create immersive digital experiences. However, the evolution of the Metaverse is slowed down by the challenges of content creation, scalability, and dynamic user interaction. Our study investigates an integration of Mixture of Experts (MoE) models with Generative Artificial Intelligence (GAI) for mobile edge computing to revolutionize content creation and interaction in the Metaverse. Specifically, we harness an MoE model's ability to efficiently manage complex data and complex tasks by dynamically selecting the most relevant experts running various sub-models to enhance the capabilities of GAI. We then present a novel framework that improves video content generation quality and consistency, and demonstrate its application through case studies. Our findings underscore the efficacy of MoE and GAI integration to redefine virtual experiences by offering a scalable, efficient pathway to harvest the Metaverse's full potential. © 2024, CC BY.

关键词： Augmented reality

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RoboRover – A Jetson Nano Powered Robot Car for Object Following, Obstacle Avoidance, and Wall Following

RoboRover – A Jetson Nano Powered Robot Car for Object Foll...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Trinh Huynh James Walter Federica Aveta Robotics Department University of Michigan Ann Arbor Michigan USA Electrical and Computer Engineering Program Wentworth Insitute of Technology Boston Massachusetts USA

An intelligent and self-sufficient robot is essential across a wide range of fields, including transportation, industry, space exploration, and defense. Mobile robots possess the capability to undertake diverse tasks such as handling materials, aiding in disaster scenarios, conducting patrols, and executing rescue operations. As a result, the development of an autonomous robot that can navigate through both unchanging and ever-changing surroundings has become important. The primary objective of mobile robot navigation revolves around ensuring the seamless and secure traversal of the robot through complex environments, starting from an initial position, and reaching a designated goal position. This paper presents the design and implementation of a Jetson Nano powered robot car which uses local sensors to interact with an unknown environment. Object following, obstacle avoidance, and wall following features are built for the car to navigate to reach its desired destinations.

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Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation

arXiv

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arXiv 2023年

作者： Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Kim, Dong In Shen, Xuemin The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada

Artificial Intelligence Generated Content (AIGC) Services have significant potential in digital content creation. The distinctive abilities of AIGC, such as content generation based on minimal input, hold huge potential, especially when integrating with semantic communication (SemCom). In this paper, a novel comprehensive conceptual model for the integration of AIGC and SemCom is developed. Particularly, a content generation level is introduced on top of the semantic level that provides a clear outline of how AIGC and SemCom interact with each other to produce meaningful and effective content. Moreover, a novel framework that employs AIGC technology is proposed as an encoder and decoder for semantic information, considering the joint optimization of semantic extraction and evaluation metrics tailored to AIGC services. The framework can adapt to different types of content generated, the required quality and the semantic information utilized. By employing a Deep Q Network (DQN), a case study is presented that provides useful insights into the feasibility of the optimization problem and its convergence characteristics. © 2023, CC BY.

关键词： Wireless networks

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LTE/WI-FI COEXISTENCE ON A FLEXIBLE SOFTWARE DEFINED RADIO TESTBED 57

LTE/WI-FI COEXISTENCE ON A FLEXIBLE SOFTWARE DEFINED RADIO T...

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57th Annual International Telemetering Conference, ITC 2022

作者： Elahi, Mirza M. Valdez, Kevin J. Gonzalez, Virgilio Computational Science Program The University of Texas at El Paso El PasoTX79968 United States Department of Electrical and Computer Engineering The University of Texas at El Paso El PasoTX79968 United States

The rapid rise in usage of mobile devices have not shown any signs of flattening or slowing down. Some efforts in the standardization bodies are underway to define new ways to boost data rate, network capacity and lower the latency by enhancing existing 4G technologies and by incorporating new technologies in 5G. Since lower spectrum has fewer resources, the LTE of unlicensed spectrum LTE-U is being proposed to extend LTE to unlicensed spectrum. Streaming data traffic for licensed spectrum on LTE-U can improve the performance of the system significantly. In this paper LTE-U deployment by the LAA is implemented using the LBT mechanism on a flexible SDR testbed. © 2022 International Foundation for Telemetering. All rights reserved.

关键词： Testbeds

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MAMMOS: MApping Multiple human MOtion with Scene understanding and natural interactions

MAMMOS: MApping Multiple human MOtion with Scene understandi...

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International Conference on computer Vision Workshops (ICCV Workshops)

作者： Donggeun Lim Cheongi Jeong Young Min Kim Dept. of Electrical and Computer Engineering Seoul National University Samsung Research Interdisciplinary Program in Artificial Intelligence and INMC Seoul National University

We present MAMMOS, an automated framework that generates the motions of multiple humans that naturally interact with each other in a given 3D scene. Many practical VR scenarios require creating dynamic human characters in harmony with the surrounding environment and other people. However, it is hard for an artist to manually generate multiple character motions tailored to the given 3D scene structure, or gather sufficient data to train an automated system that jointly considers the entangled requirements. MAMMOS is a hierarchical framework that successfully handles spatio-temporal constraints and generates high-quality motions. Given a simple tuple of action labels of the desired motion sequence, MAMMOS first places anchors in time and location for characters that avoid collisions yet enable necessary interactions. Then we generate the timelines of individual collision-free paths within the scene and connect them to perform diverse and natural motions. To the best of our knowledge, we are the first to generate long-horizon motion sequences of multiple humans with realistic interactions such that we can automatically populate the 3D scenes.

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Camera-based Motion Correction for PET/MR Brain Imaging

Camera-based Motion Correction for PET/MR Brain Imaging

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2022 IEEE Nuclear Science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Fisher, Jonathan Groll, Andrew Kim, Chris C. Innes, Derek Levin, Craig S. Stanford University Department of Electrical Engineering CA United States Molecular Imaging Program Stanford University Department of Radiology CA United States Stanford University Department of Computer Science CA United States Stanford University Physics Electrical Engineering and Bioengineering Department of Radiology CA United States

ISBN: (纸本)9781665488723

In this work, we evaluated the performance of a camera-based rigid body motion correction solution in PET studies. We compared the image quality obtained by reconstructing a static phantom scan to those obtained by reconstructing a moving phantom scan with and without applying motion correction. Our suggested solution uses a camera to track a 3D marker attached to the object to be imaged (in this work, a resolution phantom). This data was incorporated in the multiple acquisition frames (MAF) image reconstruction algorithm to compensate for motion. While we got multiple spatially shifted replicas of the resolution phantom in the motion study's reconstructed image when motion correction was not applied, the motion-corrected image was comparable to the static image, with some degradation in spatial resolution (3.2-4.2 vs. 2.8-3.2 mm) and lower peak to valley ratio (indicating lower contrast to noise ratio). The context of the results presented is in PET/MR imaging, but in principle, this motion compensation method could be used in any type of medical imaging modality. © 2022 IEEE.

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The modified Allen test and a novel objective screening algorithm for hand collateral circulation using differential photoplethysmography for preoperative assessment: a pilot study

引用

Journal of Medical engineering and Technology 2020年第2期44卷 82-93页

作者： Elwali, Ahmed Moussavi, Zahra Biomedical Engineering Program University of Manitoba Winnipeg Canada Electrical and Computer Engineering Department University of Manitoba Winnipeg Canada

Hand-ischemia and artery-thrombosis are serious complications of hand artery cannulation/grafting. Screening the patency of hand arteries reduces the complications. Modified Allen Test (MAT), Barbeau's test, and Doppler/Duplex-ultrasonic test screen hand arterial patency. Each of them, however, either lacks objectivity, requires subject cooperation, or is not readily available in operating rooms. In this pilot study, we aim to provide a novel, objective, reliable, and inexpensive alternative using differential photo-plethysmography (PPG) measurement, and an artery selection algorithm. For 11 participants, MAT was executed, and PPG measurements of the thumb and little fingers were recorded for left and right hands separately. For each hand, four evaluations were conducted, then fed into the artery selection algorithm. Our proposed procedure has shown congruent results with the anatomy of blood circulation. It identified two arteries in two participants causing >50% blood reduction in the hand due to their occlusion. It also showed an elevated risk of accessing an artery in another 8 participants. Once the results of this study are confirmed in larger samples, the proposed procedure/algorithm may provide many indicators of the hand blood circulation, the effect of an artery occlusion on the hand, and a recommendation for selecting the best artery to access with the lowest possible complications. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Diseases

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Digital Phenotype Representation by Statistical, Information Theory, Data-Driven Approach with Digital Health Data

Digital Phenotype Representation by Statistical, Information...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Binh Nguyen Michael Nigro Alice Rueda Venkat Bhat Sri Krishnan Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan Univerity Toronto ON Interventional Psychiatry Program St. Michael’s Hospital Toronto

Digital phenotyping (DP) is a multidisciplinary field of science that quantifies the individual level phenotype through active and passive data. Although DP is a multidisciplinary field, there lacks a technical and a systematic approach to representing DP. This work proposes the development of digital phenotype profile (DPP) to represent a user’s physical and behavioural health baseline through systematic investigations with an emphasis on robustness and explainability. To achieve this, a Statistical, Information Theory, and Data-driven (SID) pipeline will develop the foundation of the DPP. SID evaluates the non-linearity of the signal to offer inference for domain-specific feature extraction, evaluates the information theory to rank the DPP parameters, and imputes missing data for robust analysis, respectively. SID was applied to a 24-hr Multi-Level dataset and was able to represent individual DPPs. The respective DPPs were visualized and clusters of awake and asleep were used for individual specific modelling.

关键词： Systematics Pipelines Data visualization Signal processing Feature extraction Robustness Acoustics

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On the Powerfulness of Textual Outlier Exposure for Visual OoD Detection

arXiv

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arXiv 2023年

作者： Park, Sangha Mok, Jisoo Jung, Dahuin Lee, Saehyung Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Successful detection of Out-of-Distribution (OoD) data is becoming increasingly important to ensure safe deployment of neural networks. One of the main challenges in OoD detection is that neural networks output overconfident predictions on OoD data, make it difficult to determine OoD-ness of data solely based on their predictions. Outlier exposure addresses this issue by introducing an additional loss that encourages low-confidence predictions on OoD data during training. While outlier exposure has shown promising potential in improving OoD detection performance, all previous studies on outlier exposure have been limited to utilizing visual outliers. Drawing inspiration from the recent advancements in vision-language pre-training, this paper venture out to the uncharted territory of textual outlier exposure. First, we uncover the benefits of using textual outliers by replacing real or virtual outliers in the image-domain with textual equivalents. Then, we propose various ways of generating preferable textual outliers. Our extensive experiments demonstrate that generated textual outliers achieve competitive performance on large-scale OoD and hard OoD benchmarks. Furthermore, we conduct empirical analyses of textual outliers to provide primary criteria for designing advantageous textual outliers: near-distribution, descriptiveness, and inclusion of visual semantics. © 2023, CC BY-NC-SA.

关键词： Forecasting

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