为可靠的人工智能医学提供数据特征。

Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang, Zhiyun Xue, Sameer K Antani
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引用次数: 0

摘要

基于人工智能(AI)的医学计算机视觉算法研究有望改善疾病筛查和诊断,进而改善病人护理。然而,这些算法深受基础数据特征的影响。在这项工作中,我们将讨论影响医学计算机视觉中机器学习的设计、可靠性和演进的各种数据特征,即数据量、真实性、有效性、多样性和速度。此外,我们还将讨论每个特征以及我们研究实验室最近开展的工作,这些工作有助于我们理解这些特征对医疗决策算法设计和结果可靠性的影响。
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Data Characterization for Reliable AI in Medicine.

Research in Artificial Intelligence (AI)-based medical computer vision algorithms bear promises to improve disease screening, diagnosis, and subsequently patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely Volume, Veracity, Validity, Variety, and Velocity, that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss each characteristic and the recent works conducted in our research lab that informed our understanding of the impact of these characteristics on the design of medical decision-making algorithms and outcome reliability.

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Data Characterization for Reliable AI in Medicine. Recent Trends in Image Processing and Pattern Recognition: 5th International Conference, RTIP2R 2022, Kingsville, TX, USA, December 1-2, 2022, Revised Selected Papers Recent Trends in Image Processing and Pattern Recognition: 4th International Conference, RTIP2R 2021, Msida, Malta, December 8-10, 2021, Revised Selected Papers Recent Trends in Image Processing and Pattern Recognition: Third International Conference, RTIP2R 2020, Aurangabad, India, January 3–4, 2020, Revised Selected Papers, Part I Recent Trends in Image Processing and Pattern Recognition: Third International Conference, RTIP2R 2020, Aurangabad, India, January 3–4, 2020, Revised Selected Papers, Part II
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