从少样本分割到临床医生参与式医学图像分析
From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
AI总结：
本文提出将少样本医学图像分割重构为临床医生-模型序贯决策问题，通过交互预算和选择性专家反馈，在风险驱动下分配稀缺注意力以降低临床相关风险。
AI中文摘要：
少样本医学图像分割（FSMIS）旨在从少量支持集中描绘未见结构，但其标准范式在推理前固定了任务定义证据。当查询案例出现采集偏移、非典型病理、模糊边界或图像质量不佳时，这一假设是脆弱的。原型学习、跨域匹配、交互式分割、不确定性估计、测试时适应和可提示基础模型分别解决了该问题的部分方面，但尚未在统一的专家注意力和临床风险模型下进行联合评估。本视角将FSMIS重新定义为具有静态支持预算$K$和独立交互预算$B$的序贯临床医生-模型决策问题。在每一步中，系统接受当前分割结果、请求反馈或推迟至专家全面审查。查询在位置和模态上有所变化，并根据响应条件的净期望信息价值进行选择；临床医生提供的反馈仅在预定的来源、一致性和安全门控通过后，用于指导有界适应。该框架将分布非典型性与预测的临床失败区分开来，并将临床医生响应视为有信息但可能出错的可观察量。我们综合了从少样本和跨域分割到交互式和选择性适应的转变，描绘了整合差距，并定义了四个具有可证伪假设的研究方向。评估涵盖外部域校准、质量-努力权衡、读者研究和前瞻性工作流评估。核心主张并非交互本身能解决域偏移，而是稀缺的专家注意力应仅在预期能降低临床相关风险时被分配。
英文摘要：
Few-shot medical image segmentation (FSMIS) seeks to delineate unseen structures from a small support set, but its standard formulation fixes task-defining evidence before inference. This assumption is fragile under acquisition shift, atypical pathology, ambiguous boundaries, and poor image quality. Adding clinician interaction and rapid adaptation is not sufficient: the binding constraint is deciding when asking or changing is warranted. We therefore reframe FSMIS as a three-layer sequential decision problem. First, decidable self-assessment separates errors that a bounded intervention can repair from those that no admissible intervention can reach. We formalize this distinction through a correctable set defined by the update operator and remaining interaction budget. Second, selective interaction allocates a distinct expert-attention budget by response-conditioned net expected value of information, yielding explicit accept, query, and defer actions. Third, bounded adaptation emphasizes reversibility and independent safety reassessment rather than speed. A complementary cross-case memory stores reproducible correction priors over failure modes instead of disease-specific mask priors. This structure links sparse support representation, cross-domain robustness, multi-level risk estimation, clinician feedback, and governed experience transfer. We state six hypotheses with an explicit dependency order and propose a minimal pilot that can falsify the foundational self-assessment claim before a clinician study. The central claim is not that interaction resolves domain shift, but that scarce expert attention should be used only when a bounded intervention is expected to reach a clinically better outcome.
