用於千兆像素病理圖像分析的掩碼硬實例挖掘多實例學習框架

Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis

September 15, 2025

作者: Wenhao Tang, Sheng Huang, Heng Fang, Fengtao Zhou, Bo Liu, Qingshan Liu

cs.AI

摘要

將病理圖像數位化為千兆像素級全切片圖像（WSIs）為計算病理學（CPath）開闢了新途徑。由於陽性組織僅佔千兆像素WSIs的一小部分，現有的多實例學習（MIL）方法通常通過注意力機制來識別顯著實例。然而，這導致了對易於分類實例的偏見，而忽視了具有挑戰性的實例。最近的研究表明，困難樣本對於準確建模判別邊界至關重要。在實例層面應用這一理念，我們提出了一種新穎的MIL框架——帶有掩碼困難實例挖掘的MIL（MHIM-MIL），該框架利用具有一致性約束的孿生結構來探索困難實例。MHIM-MIL使用類感知實例概率，通過動量教師來掩碼顯著實例，並隱式挖掘困難實例以訓練學生模型。為了獲得多樣化且非冗餘的困難實例，我們採用大規模隨機掩碼，同時利用全局循環網絡來降低丟失關鍵特徵的風險。此外，學生模型通過指數移動平均更新教師模型，這有助於識別新的困難實例用於後續訓練迭代，並穩定優化過程。在癌症診斷、亞型分類、生存分析任務以及12個基準測試上的實驗結果表明，MHIM-MIL在性能和效率上均優於最新方法。代碼可在以下網址獲取：https://github.com/DearCaat/MHIM-MIL。

English

Digitizing pathological images into gigapixel Whole Slide Images (WSIs) has opened new avenues for Computational Pathology (CPath). As positive tissue comprises only a small fraction of gigapixel WSIs, existing Multiple Instance Learning (MIL) methods typically focus on identifying salient instances via attention mechanisms. However, this leads to a bias towards easy-to-classify instances while neglecting challenging ones. Recent studies have shown that hard examples are crucial for accurately modeling discriminative boundaries. Applying such an idea at the instance level, we elaborate a novel MIL framework with masked hard instance mining (MHIM-MIL), which utilizes a Siamese structure with a consistency constraint to explore the hard instances. Using a class-aware instance probability, MHIM-MIL employs a momentum teacher to mask salient instances and implicitly mine hard instances for training the student model. To obtain diverse, non-redundant hard instances, we adopt large-scale random masking while utilizing a global recycle network to mitigate the risk of losing key features. Furthermore, the student updates the teacher using an exponential moving average, which identifies new hard instances for subsequent training iterations and stabilizes optimization. Experimental results on cancer diagnosis, subtyping, survival analysis tasks, and 12 benchmarks demonstrate that MHIM-MIL outperforms the latest methods in both performance and efficiency. The code is available at: https://github.com/DearCaat/MHIM-MIL.