Mamba2MIL：用于计算病理学的基于状态空间对偶性的多示例学习
Mamba2MIL: State Space Duality Based Multiple Instance Learning for Computational Pathology
AI总结：
提出基于状态空间对偶模型（SSD）的MIL框架Mamba2MIL，通过加权特征选择与序列变换方法提升整张切片图像的序列信息利用率，在多数据集上超越现有方法。
摘要：
计算病理学（CPath）显著推动了病理学的临床实践。尽管取得了进展，但作为CPath中一种极具前景的范式，多示例学习（MIL）仍面临挑战，特别是与信息利用不充分相关的问题。现有框架，例如基于卷积神经网络（CNNs）、注意力机制和选择性扫描空间状态序列模型（SSM）的框架，在融合多样特征时缺乏足够的灵活性和可扩展性，且无法有效融合多样特征。此外，当前方法未能充分挖掘与顺序相关和与顺序无关的特征，导致序列信息的利用不佳。为了解决这些局限性，我们提出了一种名为Mamba2MIL的新型MIL框架。我们的框架利用状态空间对偶模型（SSD）对整张切片图像（WSIs）的图块长序列进行建模，该模型结合加权特征选择，支持对更多分支特征进行融合处理，并可根据特定应用需求进行扩展。此外，我们引入了一种针对不同WSI大小量身定制的序列变换方法，该方法在保留局部序列信息的同时增强了序列无关特征，从而提高了序列信息的利用率。大量实验表明，Mamba2MIL超越了最先进的MIL方法。我们在多个数据集上进行了广泛实验，在几乎所有性能指标上都取得了提升。具体而言，在NSCLC数据集上，Mamba2MIL实现了0.9533的二元肿瘤分类AUC和0.8794的准确率。在BRACS数据集上，它实现了0.7986的多分类AUC和0.4981的准确率。代码可在https://github.com/YuqiZhang-Buaa/Mamba2MIL获取。
英文摘要：
Computational pathology (CPath) has significantly advanced the clinical practice of pathology. Despite the progress made, Multiple Instance Learning (MIL), a promising paradigm within CPath, continues to face challenges, particularly related to incomplete information utilization. Existing frameworks, such as those based on Convolutional Neural Networks (CNNs), attention, and selective scan space state sequential model (SSM), lack sufficient flexibility and scalability in fusing diverse features, and cannot effectively fuse diverse features. Additionally, current approaches do not adequately exploit order-related and order-independent features, resulting in suboptimal utilization of sequence information. To address these limitations, we propose a novel MIL framework called Mamba2MIL. Our framework utilizes the state space duality model (SSD) to model long sequences of patches of whole slide images (WSIs), which, combined with weighted feature selection, supports the fusion processing of more branching features and can be extended according to specific application needs. Moreover, we introduce a sequence transformation method tailored to varying WSI sizes, which enhances sequence-independent features while preserving local sequence information, thereby improving sequence information utilization. Extensive experiments demonstrate that Mamba2MIL surpasses state-of-the-art MIL methods. We conducted extensive experiments across multiple datasets, achieving improvements in nearly all performance metrics. Specifically, on the NSCLC dataset, Mamba2MIL achieves a binary tumor classification AUC of 0.9533 and an accuracy of 0.8794. On the BRACS dataset, it achieves a multiclass classification AUC of 0.7986 and an accuracy of 0.4981. The code is available at https://github.com/YuqiZhang-Buaa/Mamba2MIL.
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