幻觉与约束：超越准确率的手术工作流程识别调控
Hallucinations and Constraints : Regulating surgical workflow recognition beyond accuracy
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中文总结 AI 辅助
该研究针对医学图像处理中AI的幻觉问题，提出用线性时序逻辑谓词结合概率图模型调控，在机器人辅助子宫切除术阶段识别中准确率提约10%且消除多数拓扑错误。
中文摘要 AI 辅助
幻觉是人工智能融入医学领域的主要问题，不过在医学图像处理领域中相关探索较少。与自然语言理解和推理问题不同，确定从生物医学图像和信号中得出的预测是否合理并不那么直观明确。本文提出，拓扑错误可构成一种更易测量和调控的幻觉。针对生物医学信号分割等特定类型问题的某些属性，可将其重新表述为线性时序逻辑谓词，其中许多谓词可使用概率图模型显式强制执行。我们的模拟实验针对机器人辅助子宫切除术的自动手术阶段识别场景，显示了这些显式约束谓词的潜力：在准确率提升约10%的同时，消除了绝大多数拓扑错误，这表明正确性的数学保证可补充医学图像计算与计算机辅助干预中机器学习的其他经验调控方式。
英文摘要
Hallucinations are a major concern for the integration of artificial intelligence into medicine, although less explored in the realm of medical image processing. Unlike problems in natural text understanding and reasoning therewith, determining whether or not predictions derived from biomedical images and signals is less intuitively clear. This article suggests that topological errors could constitute hallucinations in a way that can be more readily measured and thus regulated. Certain of these properties for certain types of problems, such as biomedical signal segmentation, can be rephrased as linear temporal logic predicates, a number of which can be explicitly enforced using probabilistic graphical models. Our simulations show the potential of these explicitly constrained predicates for the case of automatic surgical phase recognition in robot-assisted hysterectomy, improving accuracy by approximately 10% while removing the vast majority of topological errors, suggesting that mathematical guarantees of correctness can supplement other empirical forms of regulating machine learning in medical image computing and computer-assisted interventions.
发表机构
- Université de Rennes（雷恩大学）
- Inserm（法国国家健康与医学研究院）
机构由 AI 辅助整理，请以论文原文为准。
