让病理诊断模型学会区分症状优先级，提升关键病灶识别能力。
Priority-Aware Clinical Pathology Hierarchy Training for Multiple Instance Learning
- 构建垂直与水平双层次优先级结构，引导模型关注重要病理特征。
- 在真实患者数据上显著降低误诊率，关键症状识别准确率提升明显。
- 适合临床辅助诊断场景，尤其对多症状复杂病例有实用价值。
多重实例学习（MIL）正日益成为临床病理诊断决策的有力支持工具，表现出高精度并减轻标注负担。然而，现有临床MIL方法未充分考虑病理症状与诊断类别间的优先级关系，导致模型忽略类别间的重要程度差异。为克服这一临床局限，本文提出一种新方法，通过构建垂直跨层级与水平同层级双重视角的优先级层次结构，使MIL预测在各层级间保持一致，并在训练中引入隐式特征复用机制，以强化同一层级中临床更严重的类别。基于真实患者数据的实验表明，该方法有效降低了误诊率，并在多分类场景下优先识别关键症状。进一步分析验证了所提组件的有效性，且定性评估确认了模型在多重症状挑战病例中的预测可靠性。
原文摘要 · Abstract (English)
Multiple Instance Learning (MIL) is increasingly being used as a support tool within clinical settings for pathological diagnosis decisions, achieving high performance and removing the annotation burden. However, existing approaches for clinical MIL tasks have not adequately addressed the priority issues that exist in relation to pathological symptoms and diagnostic classes, causing MIL models to ignore priority among classes. To overcome this clinical limitation of MIL, we propose a new method that addresses priority issues using two hierarchies: vertical inter-hierarchy and horizontal intra-hierarchy. The proposed method aligns MIL predictions across each hierarchical level and employs an implicit feature re-usability during training to facilitate clinically more serious classes within the same level. Experiments with real-world patient data show that the proposed method effectively reduces misdiagnosis and prioritizes more important symptoms in multiclass scenarios. Further analysis verifies the efficacy of the proposed components and qualitatively confirms the MIL predictions against challenging cases with multiple symptoms.
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