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Uncertainty for safe utilization of machine learning in medical imaging : 5th international workshop, UNSURE 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, proceedings. UNSURE (Workshop) (5th : 2023...最新文献

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Benchmarking Scalable Epistemic Uncertainty Quantification in Organ Segmentation. 器官分割中可扩展的认识不确定性量化基准。
Jadie Adams, Shireen Y Elhabian

Deep learning based methods for automatic organ segmentation have shown promise in aiding diagnosis and treatment planning. However, quantifying and understanding the uncertainty associated with model predictions is crucial in critical clinical applications. While many techniques have been proposed for epistemic or model-based uncertainty estimation, it is unclear which method is preferred in the medical image analysis setting. This paper presents a comprehensive benchmarking study that evaluates epistemic uncertainty quantification methods in organ segmentation in terms of accuracy, uncertainty calibration, and scalability. We provide a comprehensive discussion of the strengths, weaknesses, and out-of-distribution detection capabilities of each method as well as recommendations for future improvements. These findings contribute to the development of reliable and robust models that yield accurate segmentations while effectively quantifying epistemic uncertainty.

基于深度学习的器官自动分割方法在辅助诊断和治疗规划方面大有可为。然而,量化和理解与模型预测相关的不确定性在关键的临床应用中至关重要。虽然已经提出了许多用于认识或基于模型的不确定性估计的技术,但目前还不清楚哪种方法在医学图像分析中更受欢迎。本文介绍了一项综合基准研究,从准确性、不确定性校准和可扩展性方面评估了器官分割中的认识不确定性量化方法。我们全面讨论了每种方法的优缺点和分布外检测能力,并对未来的改进提出了建议。这些发现有助于开发可靠、稳健的模型,在有效量化认识不确定性的同时获得准确的分割结果。
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Uncertainty for safe utilization of machine learning in medical imaging : 5th international workshop, UNSURE 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, proceedings. UNSURE (Workshop) (5th : 2023...
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