解决病理切片图像长序列建模难题，提升诊断精度。
MambaMIL+: Modeling Long-Term Contextual Patterns for Gigapixel Whole Slide Image
- 用重叠扫描重构切片序列，增强空间连续性
- 在20个基准上均达领先效果，显著优于现有方法
- 适合大规模病理分析，尤其对低标注数据有效
全切片图像（WSI）是计算病理学的重要数据模态，但其吉比特级分辨率和缺乏细粒度标注给传统深度学习模型带来挑战。多实例学习（MIL）通过将每张WSI视为一组局部块实例来应对，但高效建模超长序列的丰富空间上下文仍困难。近期，Mamba因其线性扩展性成为长序列学习的有力候选，但仍受限于空间上下文建模不足与记忆衰减问题。为此，我们提出MambaMIL+，一个新式MIL框架，在保持长程依赖建模的同时显式融合空间上下文且无记忆遗忘。具体包括：1）重叠扫描，重构块序列以嵌入空间连续性和实例关联；2）选择性条带位置编码器（S2PE），编码位置信息并缓解固定扫描顺序带来的偏差；3）上下文令牌选择机制（CTS），利用监督知识动态扩大上下文记忆，实现稳定长程建模。在涵盖诊断分类、分子预测与生存分析的20个基准上，使用三种特征提取器（ResNet-50、PLIP、CONCH）进行大量实验，结果表明MambaMIL+持续达到最优性能，凸显其在大规模计算病理中的有效性与鲁棒性。
原文摘要 · Abstract (English)
Whole-slide images (WSIs) are an important data modality in computational pathology, yet their gigapixel resolution and lack of fine-grained annotations challenge conventional deep learning models. Multiple instance learning (MIL) offers a solution by treating each WSI as a bag of patch-level instances, but effectively modeling ultra-long sequences with rich spatial context remains difficult. Recently, Mamba has emerged as a promising alternative for long sequence learning, scaling linearly to thousands of tokens. However, despite its efficiency, it still suffers from limited spatial context modeling and memory decay, constraining its effectiveness to WSI analysis. To address these limitations, we propose MambaMIL+, a new MIL framework that explicitly integrates spatial context while maintaining long-range dependency modeling without memory forgetting. Specifically, MambaMIL+ introduces 1) overlapping scanning, which restructures the patch sequence to embed spatial continuity and instance correlations; 2) a selective stripe position encoder (S2PE) that encodes positional information while mitigating the biases of fixed scanning orders; and 3) a contextual token selection (CTS) mechanism, which leverages supervisory knowledge to dynamically enlarge the contextual memory for stable long-range modeling. Extensive experiments on 20 benchmarks across diagnostic classification, molecular prediction, and survival analysis demonstrate that MambaMIL+ consistently achieves state-of-the-art performance under three feature extractors (ResNet-50, PLIP, and CONCH), highlighting its effectiveness and robustness for large-scale computational pathology
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