用组织上下文纠正淋巴细胞形态相似误判，提升病理分析准确率。
Lymphocyte Mimicry Correction via Region-Level Tissue Reasoning and Unbalanced Optimal Transport
- 通过不平衡最优传输将区域级组织推理迁移至单细胞预测
- 在独立数据集上患者级误差低于全监督模型，且在上皮丰富组织中F1提升
- 仅需278个弱标注区域估计，适合资源有限的病理分析场景
细胞模拟现象指不同细胞类型在形态上相似。人类病理科医生借助周围组织上下文解决此类歧义，而现有视觉模型或缺乏上下文推理能力（细胞基础模型），或无法在细胞层面运行（病理多模态大模型）。本文提出Loki-OT，通过不平衡最优传输将区域级组织推理传播至个体细胞预测，利用多模态大模型生成的密度先验作为软引导，对模糊细胞进行重分配。该方法基于观察：预训练细胞基础模型特征已编码区分性信息，包括组织上下文，但标准细胞级监督难以有效利用。最终的传输方案被提炼为轻量级学生MLP分类器，在预训练特征空间中学习上下文感知决策边界。在独立TCGA-BRCA队列上，Loki-OT患者级平均绝对误差低于全监督域内PanopTILs分类器，并在上皮丰富模拟组织中提升F1分数，仅使用278个基于通用领域细胞基础模型构建的弱区域级估计。
原文摘要 · Abstract (English)
Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context, whereas current vision models either lack contextual reasoning (cell foundation models) or cannot operate at the cell level (pathology MLLMs). We present Loki-OT, which propagates region-level tissue reasoning to individual cell predictions via Unbalanced Optimal Transport, using MLLM-derived density priors as soft guidance for ambiguous cell reassignment. Loki-OT is motivated by the observation that pretrained cell foundation model features already encode discriminative information, including tissue context, but standard cell-level supervision fails to use tissue context effectively. The resulting transport plan is distilled into a lightweight student MLP classifier that learns context-aware decision boundaries within the pretrained feature space. On the independent TCGA-BRCA cohort, Loki-OT achieved lower patient-level MAE than the fully supervised in-domain PanopTILs classifier and improved F1 in epithelium-rich mimicry tissues, using 278 weak region-level MLLM estimates built on a general-domain cell foundation model. Code: https://github.com/xiangli980/Lymphocyte_Mimicry_Correction_via_Loki_OT
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