在放射图像深度学习中量化不确定性
Shahriar Faghani1, Mana Moassefi1, Pouria Rouzrokh1
1From the Artificial Intelligence Laboratory (S.F., M.M., P.R., B.K., B.J.E.) and Division of Musculoskeletal Radiology (F.I.B., M.D.R.), Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN 55905.
Radiology
|August 1, 2023
概括
放射学中的深度学习 (DL) 需要不确定性量化 (UQ) 来确保可靠的预测. 通过识别DL模型缺少足够的数据来做出自信的诊断决策,UQ方法增加了信任.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
- 放射学 放射学是一门学科.
背景情况:
- 深度学习 (DL) 模型在放射图像分析中表现出高性能.
- DL模型预测的可靠性通常受到未经校准的概率的限制.
- 医疗保健中的可靠人工智能需要量化DL模型输出的不确定性.
研究的目的:
- 审查放射图像分析中深度学习 (DL) 的不确定性量化 (UQ) 的最新趋势.
- 为理解UQ在这个领域提供一个概念框架.
- 讨论UQ在医疗DL中的应用,挑战和未来方向.
主要方法:
- 在放射图像分析中应用到DL的UQ方法的文献综述.
- 对UQ在提高DL模型可信度中的作用的概念分析.
- 讨论实际实施方面和未来的研究途径.
主要成果:
- 在放射学中,UQ方法对于评估DL模型预测的有效性至关重要.
- 实施UQ提醒用户对低可信度预测进行警报,使专家能够进行审查.
- UQ提高了DL工具在医学成像中的可靠性和临床实用性.
结论:
- 不确定性量化对于放射学深度学习的安全和有效部署至关重要.
- 对UQ方法的进一步研究将提高AI驱动的诊断工具的可靠性和临床采用.
- 解决UQ中的挑战将释放AI在医学图像分析中的全部潜力.
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