有限资源下全切片病理图像AI与诊断视野选择AI的信息论分析

中文摘要 AI 辅助

将AI用于病理诊断的关键问题是应为AI提供何种图像信息，以及应如何分配有限的分析资源。本研究在有限资源下比较两种处理不同类型图像的方式：第一种是WSI-AI，即AI自动压缩全切片图像（Whole Slide Image，WSI）的信息；第二种是诊断视野选择（Diagnostic Field Selection，DFS）-AI，即由专家先选择诊断所需的多个区域、放大倍数及对比项，再由AI分析这些选定的视野。为比较这两种方式，我们构建了三种图像模型：极少定位的病灶、非均匀背景中的病灶检测、空间连续的病灶。结果显示，当粗视图无法预先提供足够的病灶位置信息时，WSI-AI表现更优，例如淋巴结中极小肿瘤灶的情况；相反，当低成本粗视图能提供有用的位置信息时，在有限资源的中间范围内，DFS-AI表现更好。当在非均匀背景中搜索特定目标（如感染性微生物）时，DFS-AI的相对价值会提升。对于空间连续的病灶，随着病灶变大，WSI-AI更易检测到病灶的存在；但要对整个病灶进行完整表征，DFS-AI可能更优，因为它会利用连续结构来选择分析视野。结论是，两者的相对性能会随粗视图提供的位置信息、背景异质性、局部上下文及病灶的空间结构发生系统性变化。有限资源下的实用策略是，医师应根据诊断任务的信息结构在WSI-AI和DFS-AI之间做出选择。

英文摘要

An important question in pathology AI is how to allocate limited analytical resources between broad coverage of a whole slide image (WSI) and detailed analysis of selected regions. We compared a minimal WSI-AI model designed for broad search with Diagnostic Field Selection AI (DFS-AI), which uses a coarse overview to select diagnostically relevant fields for detailed analysis. Three minimal models introduced localization information, contextual discrimination, and spatial structure in sequence. In Model I, WSI-AI was favored when coarse localization was uninformative or costly. When coarse observation provided useful ranking information, DFS-AI was favored over an intermediate range of resources; WSI-AI again became preferable when resources allowed nearly complete fine observation. In Model II, background heterogeneity generated distractors. In a WSI control given the same local context as DFS-AI, most of the original DFS advantage was explained by local background correction, while coarse candidate selection provided a smaller additional benefit. In Model III, larger contiguous lesions were easier for WSI-AI to discover but more difficult to characterize completely with a finite budget for fine observation. The DFS advantage for complete characterization persisted when the comparator used the same field sized unit of fine analysis and when success was defined symmetrically as 100%, 90%, or 80% lesion unit coverage. Together, the models support a framework in which the preferred strategy changes with available resources and depends on the information available for selection, its acquisition cost, lesion structure, and the diagnostic endpoint. This framework clarifies when limited pathology AI resources should be devoted to broad WSI coverage and when they should be concentrated on selected Diagnostic Fields.

发表机构

• Department of Drug Discovery Medicine, Graduate School of Medicine, Kyoto University（京都大学大学院医学研究系药物发现医学学科）

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