Data collection and quality challenges for deep learning

Whang, Steven Euijong; Lee, Jae-Gil

doi:10.14778/3415478.3415562

Cited by 74 publications

(27 citation statements)

References 55 publications

(85 reference statements)

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“…There is some guidance 71 on discretion referring to IMDRF policy. 72 Thus, the use of the MLMD application in a "safe" context may support a lower risk classification. Yet many MLMD devices may end up as high-risk devices that will be covered by mandatory regulation.…”

Section: The Institute Of Electrical and Electronics Engineers (Ieee)mentioning

confidence: 99%

“…71 https://ec.europa.eu/health/system/files/2020-09/md_mdcg_2019_11_ guidance_qualification_classification_software_en_0.pdf accessed 27.4.2022. 72 check additional requirements according to "General Features of Medical Device Regulation" section 10. Their questions are reflected by the AI-guideline of the Johner Institute.…”

Section: The Institute Of Electrical and Electronics Engineers (Ieee)mentioning

confidence: 99%

“…Thus, data governance in MLMD is a fundamental issue. A data-centric life cycle approach to MLMD is advocated for ethical (71) and technical reasons (72) and may be particularly helpful in health RWD that generally lacks maintenance and validation and raises many interoperability problems.…”

Section: Summary and Reflections On The Future Of Regulationmentioning

confidence: 99%

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Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging

Wellnhofer

2022

Front. Cardiovasc. Med.

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Recent progress in digital health data recording, advances in computing power, and methodological approaches that extract information from data as artificial intelligence are expected to have a disruptive impact on technology in medicine. One of the potential benefits is the ability to extract new and essential insights from the vast amount of data generated during health care delivery every day. Cardiovascular imaging is boosted by new intelligent automatic methods to manage, process, segment, and analyze petabytes of image data exceeding historical manual capacities. Algorithms that learn from data raise new challenges for regulatory bodies. Partially autonomous behavior and adaptive modifications and a lack of transparency in deriving evidence from complex data pose considerable problems. Controlling new technologies requires new controlling techniques and ongoing regulatory research. All stakeholders must participate in the quest to find a fair balance between innovation and regulation. The regulatory approach to artificial intelligence must be risk-based and resilient. A focus on unknown emerging risks demands continuous surveillance and clinical evaluation during the total product life cycle. Since learning algorithms are data-driven, high-quality data is fundamental for good machine learning practice. Mining, processing, validation, governance, and data control must account for bias, error, inappropriate use, drifts, and shifts, particularly in real-world data. Regulators worldwide are tackling twenty-first century challenges raised by “learning” medical devices. Ethical concerns and regulatory approaches are presented. The paper concludes with a discussion on the future of responsible artificial intelligence.

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Section: The Institute Of Electrical and Electronics Engineers (Ieee)mentioning

confidence: 99%

Section: The Institute Of Electrical and Electronics Engineers (Ieee)mentioning

confidence: 99%

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Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging

Wellnhofer

2022

Front. Cardiovasc. Med.

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“…This type of study focuses on the analysis of the population involved in the research study (Xie et al 2018). Surveys conducted with the involvement of the cross-sectional study are always descriptive and trends to provide an advantage to the researchers in the smooth conduction of the research (Whang and Lee, 2020). A major focus is to do data collection depending on the different types of situations that may occur.…”

Section: Time Horizonmentioning

confidence: 99%

Digital Influencers and their impact on purchasing decisions of Millennials to buy Make Up products

Sakib¹

2022

Preprint

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This challenge makes a specialty of the impact and moves of a virtual influencer in terms of the acquisition of merchandise through the web platform. In this challenge, the principal purpose of virtual influencers is to take advantage of people’s belief and inspire them to shop for product-unique makeup. It all relies upon at the influencer's advertising strategies, and the challenge is centered on course target market while shopping for splendor merchandise. In this project, the evaluation of various people's responses at the social media platform became tremendously clear to make from different magazine articles. The respondents have been taken exclusively as male and female fore this research to be performed. The literature evaluate phase includes all of the distinctive parameters related to the virtual influencer. The proposed technique related to the translation of the effect of virtual influencers at the patron related to the acquisition of splendor products.

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“…Most of the effort in the application of machine learning algorithms is spent on data preparation [11] . The performance of a machine learning algorithm is directly related to the quality of the data sets employed for the learning task.…”

Section: Data Generation and Managementmentioning

confidence: 99%

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

Darrah¹,

Biswas²,

Frank³

et al. 2022

J Surveill Secur Saf

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Maintaining safe operations in cyber physical systems is a complex task that must account for system degradation over time, since unexpected failures can result in the loss of life and property. Operational failures may be attributed to component degradation and disturbances in the environment that adversely impact system performance. Components in a CPS typically degrade at different rates, and, therefore, require continual monitoring to avoid unexpected failures. Moreover, the effects of multiple degrading components on system performance may be hard to predict. Developing and maintaining accurate physics-based system models can be expensive. Typically, it is infeasible to run a true system to failure, so researchers and practitioners have resorted to using data-driven techniques to better evaluate the effect of degrading components on overall system performance. However, sufficiently organized datasets of system operation are not readily available; the output of existing simulations is not organized to facilitate the use of data-driven machine learning techniques for prognostics. As a step toward addressing this problem, in this paper, we develop a data management framework and an end-to-end simulation testbed to generate such data. The framework facilitates the development and comparison of various system-level prognostics algorithms. We adopt a standard data-centered design methodology, combined with a model based engineering approach, to create a data management framework that address data integrity problems and facilitates the generation of reproducible results. We present an ontological design methodology centered around assets, processes, and data, and, as a proof of concept, develop an unmanned aerial vehicle (UAV) system operations database that captures operational data for UAVs with multiple degrading components operating in uncertain environments. Aim: The purpose of this work is to provide a systematic approach to data generation, curation, and storage that supports studies in fault management and system-level prognostics for real-world and simulated operations. We use a data-driven simulation-based approach to enable reliable and reproducible studies in system-level prognostics. This is accomplished with a data management methodology that enforces constraints on data types and interfaces, and decouples various parts of the simulation to enable proper links with related metadata. The goal is to provide a framework that facilitates data analysis and the development of data-driven models for prognostics using machine learning methods. We discuss the importance of systematic data management framework to support data generation with a simulation environment that generates operational data. We describe a standard framework for data management in the context of run-to-failure simulations, and develop a database schema and an API in MATLAB® and Python to support system-level prognostics analyses. Methods: A systematic approach to defining a data management framework for the study of prognostics applications is a central piece of this work. A second important contribution is the design of a Monte Carlo simulation environment to generate run to failure data for CPS with multiple degrading components. We adopt a bottom-up approach, starting with requirements and specifications, then move into functionality and constraints. With this framework, we use a Monte-Carlo simulation approach to generate data for developing and testing a variety of system-level prognostics algorithms. Results: We have developed a data management framework that can handle high dimensional and complex data generated from real or simulated systems for the study of prognostics. In this paper, we show the advantages of a well-organized data management framework for tracking high-fidelity data with high confidence for complex, dynamic CPS. Such frameworks impose data logging discipline and facilitate downstream uses for developing and comparing different data-driven monitoring, diagnostics, and system-level prognostics algorithms. Conclusions: In this paper, we demonstrate the design, development, and use of an asset, process, and data management framework for the research to develop prognostics & health management applications. This work helps fill a gap for system-level remaining useful life studies by providing a comprehensive simulation environment that can generate run to failure data, and a data management architecture that addresses the needs for system-level prognostics research. The framework is demonstrated with a Monte-Carlo simulation of a UAV system that operates multiple flights under different environmental conditions and degradation sources. This architecture for data management will enable researchers to conduct more complex experiments for a variety of cyber physical systems applications.

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Data collection and quality challenges for deep learning

Cited by 74 publications

References 55 publications

Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging

Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging

Digital Influencers and their impact on purchasing decisions of Millennials to buy Make Up products

A data-centric approach to the study of system-level prognostics for cyber physical systems: application to safe UAV operations

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