滑动级主动学习减轻苏木精-伊红染色图像的标注负担

中文摘要 AI 辅助

基于深度学习的组织病理学全切片图像（WSIs）分割需要大量像素级标注，获取成本高且耗时。主动学习（AL）虽被提出以减轻此负担，但现有方法有三个关键局限。我们提出SHAL（滑动级混合主动学习），一个用于高效标注的多类组织病理学分割的患者级AL框架。它集成三个互补组件：抑制未标记背景区域偏差的前景感知策略、跨学习阶段混合预测熵和认知不确定性的阶段自适应机制、优先考虑诊断相关组织类别的类别感知策略。在TCGA结直肠癌数据集上评估，SHAL在全标注预算下Macro Dice最高（0.846），仅用26%预算（190张切片中的50张）就能达到Dice≥0.80，而竞争方法在37%（70张切片）时才达到此阈值。在五个独立外部队列中，SHAL平均外部Macro Dice最高（0.815），所有方法中内部到外部的泛化差距最小（第3轮为0.025，全预算时为0.026）。结果表明患者级混合不确定性获取可降低标注成本且不牺牲计算病理学中的跨域泛化能力。

英文摘要

Deep learning-based segmentation of histopathology whole-slide images (WSIs) requires large amounts of pixel-level annotations, which are costly and time-consuming to obtain. Active learning (AL) has been proposed to reduce this effort, but existing methods exhibit three key limitations. Uncertainty estimation is unreliable on partially annotated WSIs, patch-level acquisition is inconsistent with slide-level annotation workflows, and class imbalance in multi-class settings is not explicitly addressed. To address these challenges, we propose SHAL (Slide-level Hybrid Active Learning), a patient-level AL framework for annotation-efficient multi-class histopathology segmentation. SHAL integrates three complementary components: a foreground-aware strategy that suppresses bias from unlabeled background regions, a stage-adaptive mechanism that hybridizes predictive entropy and epistemic uncertainty across learning stages, and a class-aware strategy that prioritizes diagnostically relevant tissue classes. SHAL is evaluated on the TCGA colorectal cancer dataset. It achieves the highest Macro Dice at the full annotation budget (0.846) and reaches Dice greater than or equal to 0.80 using only 26 percent of the budget (50 of 190 slides), whereas competing methods reach this threshold only at 37 percent (70 slides). Across five independent external cohorts, SHAL attains the highest mean external Macro Dice (0.815) and the smallest internal-to-external generalization gap among all methods (0.025 at Round 3 and 0.026 at the full budget). The results indicate that patient-level hybrid uncertainty acquisition reduces annotation cost without sacrificing cross-domain generalization in computational pathology.

发表机构

• Institute for Biomedical Informatics, Faculty of Medicine（生物医学信息学研究所，医学学院）
• University Hospital Cologne, University of Cologne（科隆大学医院，科隆大学）
• Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine（科隆分子医学中心（CMMC），医学学院）
• Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne（科隆卓越集群：与衰老相关疾病细胞应激反应（CECAD），科隆大学）
• Faculty of Mathematics（数学学院）
• Natural Sciences, University of Cologne（自然科学学院，科隆大学）
• Institute of Pathology, University Hospital Cologne（病理学研究所，科隆大学医院）

机构由 AI 辅助整理，请以论文原文为准。