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Sequestration methodology in practice through evaluation of joint demographic distributions of 54,185 patients in the Medical Imaging and Data Resource Center (MIDRC) data commons

Sequestration methodology in practice through evaluation of ...

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Conference on Medical Imaging - Imaging Informatics for Healthcare, Research, and Applications

作者： Baughan, Natalie Whitney, Heather M. Drukker, Karen Sahiner, Berkman Hu, Tingting Kim, Grace Hyun McNitt-Gray, Michael Myers, Kyle J. Giger, Maryellen L. Univ Chicago Dept Radiol Chicago IL 60637 USA US FDA Silver Spring MD USA Univ Calif Los Angeles Los Angeles CA USA Puente Solut LLC Sacramento CA USA MIDRC Org Med Imaging & Data Resource Ctr Chicago IL 60637 USA

ISBN: (纸本)9781510660434;9781510660441

The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available data commons as well as a sequestered commons for performance evaluation of algorithms. This work sought to evaluate the currently implemented methodology for apportioning data to the public and sequestered data commons by investigating the resulting distributions of joint demographic characteristics between the public and sequestered commons. 54,185 patients whose de-identified imaging studies and metadata had been submitted to MIDRC were previously separated into public and sequestered commons using a multi-dimensional stratified sampling method, resulting in 41,556 patients (77%) in the public commons and 12,629 patients (23%) in the sequestered commons. To compare the balance obtained in the joint distributions of patient characteristics from use of the developed sequestration method, patients from each commons were separated into bins, representing a unique combination of the demographic variables of COVID-19 status, age, race, and sex assigned at birth. The joint distributions of patients were visualized, and the absolute and percent difference in each bin from an exact 77:23 split of the data were calculated. Results indicated 75.9% of bins obtained differences of less than 15 patients, with a median difference of 3.6 from the total data for both public and sequestered commons. Joint distributions of patient characteristics in both the public and sequestered commons closely matched each other as well as that of the total data, indicating the sequestration by stratified sampling method has operated as intended.

关键词： Machine learning image database algorithm performance MIDRC AI sequestration COVID-19

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Optimal Placement algorithm for Multiple Heterogeneous Stereo Vision Systems

Optimal Placement Algorithm for Multiple Heterogeneous Stere...

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AIAA Aviation Forum and Exposition

作者： Kang, Changkoo Woolsey, Craig A. Virginia Tech Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

An unmanned aircraft system (UAS) can use a heterogeneous stereo vision system to detect and localize other aircraft which may pose a threat. The error in the triangulated location may be large, however, because of the short baseline between the two cameras mounted on the host aircraft. One way to address this issue is to use multiple host aircraft, each with a heterogeneous stereo vision system, in order to increase the stereo vision baseline. In this letter, we suggest a heterogeneous stereo vision optimal placement (HSOP) algorithm that solves a mixed-integer nonlinear programming problem to determine optimal positions for multiple airborne heterogeneous stereo vision systems in order to minimize the localization error for multiple threats. After introducing camera models for the "peripheral-central vision system," we formulate an optimization problem that supports counter-UAS applications. To assess the algorithm's performance, the simulated localization error of the HSOP algorithm is compared with that of another placement algorithm that considers only threat coverage. The results indicate that the HSOP algorithm effectively reduces the threat localization error.

关键词： Unmanned Aircraft System algorithm performance Nonlinear Programming MATLAB Energy Consumption Image Resolution Search algorithm Image Processing Computer Vision Motion Planning

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APIS: Honeybee Foraging Task Assignment for Use in Uncertain and Unreliable Environments

APIS: Honeybee Foraging Task Assignment for Use in Uncertain...

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AIAA SciTech Forum

作者： Pye, John E. Alexandrov, Natalia M. NASA Langley Res Ctr Autonomous Integrated Syst Res Branch Hampton VA 23681 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Multiagent Cyber-Physical-Human (CPH) systems in realistic environments operate under uncertain conditions. Communication among agents, aimed at reducing the uncertainty, is itself subject to uncertainty. We propose to manage uncertainties in autonomous, long-duration operations of multiagent systems via a modified Honeybee Foraging (HBF) behavioral scheme. The resulting system, Autonomous Persistent Intelligent Swarm (APIS), incorporates two new behaviors to ameliorate informational uncertainty. When "scouting", agents are tasked based on informational quality and reliability rather than solely on priorities. When "dancing", agents are tasked to rendezvous with other dancing agents to exchange information at close range, where successful communication is guaranteed. When coupled with uncertainty-aware modeling across agents, these behaviors improve situational awareness and resilience of the system, enabling it to function under more uncertain conditions arising during long-duration missions.

关键词： Ground Control Station Autonomous Systems Obstacle Avoidance Artificial Bee Colony algorithm performance Collision Avoidance Mathematical Models

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Assessment of computerized algorithms by comparing with human observers in binary classification tasks: a simulation study

Assessment of computerized algorithms by comparing with huma...

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Conference on Medical Imaging - Image Perception, Observer performance, and Technology Assessment

作者： Yang, Yang Sahiner, Berkman Huang, Zhipeng Petrick, Nicholas Chen, Weijie US FDA Div Imaging Diagnost & Software Reliabil Off Sci & Engn Labs Ctr Devices & Radiol Hlth Rockville MD 20857 USA SUNY Buffalo Sch Publ Hlth & Hlth Profess Dept Biostat Buffalo NY USA

ISBN: (数字)9781510616448

ISBN: (纸本)9781510616448

It is generally recognized that recent advancements in computer vision, especially the development of deep convolutional neural networks, has substantially improved the performance of computerized algorithms in medical imaging for classification tasks such as cancer detection/*** advancements underscore the importance of the question of how the computer algorithm's stand-alone performance compares with the performance of physicians. Current literature often uses descriptive statistics or a visual check of plots for the comparison lacking quantitative and rigorous statistical inference. In this work, we developed a U-statistic based approach to estimate the variance of performance difference between an algorithm and a group of human observers in a binary classification task. The performance metric considered in this work is percent correct (PC), e.g., sensitivity or specificity. Our variance estimation treats both human observers and patient cases as random samples and accounts for both sources of variability, thereby allowing for the conclusion to be generalizable to both the patient and the physician populations. Moreover, we investigated a z-statistic method based on our variance estimator for hypothesis testing. Our simulation results show that our variance estimator for the PC performance difference is unbiased. The normal approximation method using our variance estimator for hypothesis testing appears useful for large sample sizes.

关键词： technology assessment algorithm performance variance estimation MRMC

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Effects of Silent Intervals on the Extraction of Human Frequency-Following Responses Using Non-Negative Matrix Factorization

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PERCEPTUAL AND MOTOR SKILLS 2023年第5期130卷 1834-1851页

作者： Giordano, Allison T. T. Jeng, Fuh-Cherng Black, Taylor R. R. Bauer, Sydney W. W. Carriero, Amanda E. E. McDonald, Kalyn Lin, Tzu-Hao Wang, Ching-Yuan Ohio Univ Commun Sci & Disorders Athens OH USA Acad Sinica Biodivers Res Ctr Taipei Taiwan China Med Univ Hosp Dept Otolaryngol HNS Taichung 404 Taiwan

Source-Separation Non-Negative Matrix Factorization (SSNMF) is a mathematical algorithm recently developed to extract scalp-recorded frequency-following responses (FFRs) from noise. Despite its initial success, the effects of silent intervals on algorithm performance remain undetermined. Our purpose in this study was to determine the effects of silent intervals on the extraction of FFRs, which are electrophysiological responses that are commonly used to evaluate auditory processing and neuroplasticity in the human brain. We used an English vowel /i/ with a rising frequency contour to evoke FFRs in 23 normal-hearing adults. The stimulus had a duration of 150 ms, while the silent interval between the onset of one stimulus and the offset of the next one was also 150 ms. We computed FFR Enhancement and Noise Residue to estimate algorithm performance, while silent intervals were either included (i.e., the WithSI condition) or excluded (i.e., the WithoutSI condition) in our analysis. The FFR Enhancements and Noise Residues obtained in the WithoutSI condition were significantly better (p < .05) than those obtained in the WithSI condition. On average, the exclusion of silent intervals produced a 11.78% increment in FFR Enhancement and a 20.69% decrement in Noise Residue. These results not only quantify the effects of silent intervals on the extraction of human FFRs, but also provide recommendations for designing and improving the SSNMF algorithm in future research.

关键词： electroencephalographic machine learning lexical tone frequency-following response silent interval non-negative matrix factorization algorithm performance

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A Hybrid Approach to Detection and Tracking of Unmanned Aerial Vehicles

A Hybrid Approach to Detection and Tracking of Unmanned Aeri...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Esposito, Nicola Fontanat, Umberto D'Autilia, Giampiero Bianchi, Lorenzo Alibani, Michael Pollini, Lorenzo Univ Pisa Fac Automat Engn Dipartimento Ingn Informaz I-56126 Pisa Italy

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

This paper proposes a novel, hybrid vision-based system to autonomously detect and track a specific moving target, in our case an evader UAV (Unmanned Aerial Vehicles), with a moving camera. The framework is based on a detection stage which exploits a Faster Region-based Convolutional Neural Network (Faster R-CNN) designed to detect the Region Of Interest (ROI) associated to the UAV's position in the image plane. The moving target is tracked by using an Optical Flow-based tracking system and a Kalman Filter is used to give temporal consistency between consecutive measurements. The tracking system is designed to be able to achieve real-time image processing on embedded systems, for this reason a lag compensation algorithm for the delay due to the Faster R-CNN computation time is implemented. algorithm's performance is evaluated by computing the error between the true UAV position in the image plane and the estimated position resulting from the tracking system.

关键词： Unmanned Aerial Vehicle Convolutional Neural Network Kalman Filter Embedded Systems algorithm performance Image Processing Computing Track algorithm Search algorithm Corner Detection

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Acceleration of Adjoint-Based Adaptation through Sub-Iterations for Unsteady Simulations

Acceleration of Adjoint-Based Adaptation through Sub-Iterati...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Ding, Kaihua Fidkowski, Krzysztof J. Univ Michigan Dept Aerosp Engn Ann Arbor MI 48105 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

Sub-iterations in adjoint-based adaptation refer to reusing primal states and fine-space adjoints, in multiple adaptive iterations. This technique has been demonstrated for steady compressible Navier-Stokes simulations, for which it achieved significant CPU time savings as compared to standard adjoint-based adaptation. In this paper, we extend the sub-iterations idea of formulating approximated and relatively accurate adaptive indicators by reusing solver information, to unsteady problems. The steady sub-iteration only needs to reconstruct in space in order to reuse the previous adaptation iteration's primal states and fine-space adjoints, while unsteady sub-iterations require both spatial and temporal reconstruction due to mismatched adapted time histories. In this paper, we present our results for simulations of the unsteady Navier-Stokes equations, with p adaptation.

关键词： Navier Stokes Simulation Vortex Shedding Phenomena Drag Coefficient Advection Prandtl Number Cost Analysis Angle of Attack NACA 0012 Viscosity algorithm performance

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Autonomous Flightworthiness Determination for Modular Vertical Lift Vehicles

Autonomous Flightworthiness Determination for Modular Vertic...

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AIAA Guidance, Navigation, and Control Conference and Exhibit

作者： Warner, Jonathan Rogers, Jonathan Georgia Inst Technol Woodruff Sch Mech Engn Atlanta GA 30332 USA

ISBN: (数字)9781624104503

ISBN: (纸本)9781624104503

Payload transportation via connected modular unmanned aerial vehicles is an emerging new area that offers unique advantages over other forms of aerial logistics. When considering rigidly attached, modular, vertical lift, unmanned aerial vehicles, differing payloads and vehicle attachment geometries have a significant effect on the composite aircraft's dynamic response during takeoff and stabilization. With no prior knowledge of payload parameters or vehicle attachment geometry, there is no inherent flightworthiness guarantee for a specific connected configuration. Onground flightworthiness determination can be used to ensure acceptable performance during vehicle takeoff or to prescribe changes to the vehicle attachment geometry if necessary. This paper introduces an algorithm to determine flightworthiness while in partial ground contact by estimating the vehicle attachment positions and payload weight. The algorithm uses a probabilistic estimate of vehicle placement about the payload derived through a Bayesian learning technique to generate the necessary data to deterministically estimate the attached vehicles' positions. Following a description of the algorithm, simulation results are presented to illustrate the performance of the algorithm for a variety of modular aircraft configurations.

关键词： Thrust to Weight Ratio Unmanned Aerial Vehicle Aircraft Configurations Dynamic Response Pitching Moment Body Reference Frame Quadrotor algorithm performance Monte Carlo Simulation Dynamic Analysis

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Utilization of Constraint Satisfaction Problem algorithms in Model-Based Safety Analysis

Utilization of Constraint Satisfaction Problem Algorithms in...

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AIAA Aviation Forum

作者： Elmeadawy, Samar Abdellatif, Akram Amin Holzapfel, Florian German Univ Cairo Informat Engn & Technol New Cairo 11511 Egypt Tech Univ Munich Flight Syst Dynam Boltzmannstr 15 D-85748 Garching Germany

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

Model-Based Safety Analysis (MBSA) is an introduced approach in the safety analysis field which intends to connect the physical design of a system to the safety of its operation. Both design engineers and safe engineers use a common model consisting of the physical behavior in addition to the failure behavior of a system. Such method can reduce the time and cost of the safety analysis process. A pre-implemented tool represents a new developed qualitative safety analysis tool utilizing the MBSA approach. Although the tool has shown decent results with various systems but it has some defects. The tool mainly utilized directional traversal algorithms for failure injections in the system. The tool worked correctly with a system of small number of interconnected components capturing all required failure combinations to be tested. With the increase of system components, the algorithm failed to capture redundant interconnected components but stayed looping through the system components without termination. Thus, a new systematic algorithm must be used. In this paper, a Constraint Satisfaction Problem (CSP) technique "Backtracking" is tested on some of huge complex systems. The algorithm utilizes the CSP along with inference logic to rank the interconnected components according to failure impact on the whole system. Ranking the system components reduces the operation complexity by a huge amount and prevents the brutal force of testing all failure combinations. The developed algorithm is verified by testing upon various systems as a conclusion of this work.

关键词： Busbar Failure Modes and Effects Analysis Graphical User Interface algorithm performance Search algorithm Software Tool Gear Retraction Black Box Inverters

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Centralized Autonomous Relative Navigation of Multiple Spacecraft Around Small Bodies

Centralized Autonomous Relative Navigation of Multiple Space...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Silvestrini, Stefano Capannolo, Andrea Piccinin, Margherita Lavagna, Michele Fernandez, Jesus Gil Politecn Milan Dept Aerosp Sci & Technol Via La Masa 34 I-20156 Milan Italy European Space Agcy ESTEC ESA ESTEC Keplerlaan 1 NL-2201 AZ Noordwijk Netherlands

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

This paper presents an on-board, centralized autonomous navigation algorithm able to reconstruct the trajectories of a fleet of CubeSats relative to an asteroid binary system. The algorithm is executed on-board the main spacecraft, which takes relative measurements using a narrow optical camera and an additional relative measurement, being inter satellite ranging in this paper. The image processing algorithm detects and tracks the CubeSats, deriving line-of-sight measurements. A non-linear estimation filter is employed to reconstruct the trajectory. The algorithm is tested using high-fidelity trajectory simulator and synthetic images generation routines.

关键词： Spacecrafts Satellites Asteroids Autonomous Navigation Image Processing Unscented Kalman Filter Hyperbolic Trajectories Probability Density Functions Vision Based Navigation algorithm performance

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