DeltaMIL: 基于门控记忆的高效且判别性全滑片图像分析
DeltaMIL: Gated Memory Integration for Efficient and Discriminative Whole Slide Image Analysis
AI总结：
DeltaMIL通过门控记忆机制提升全滑片图像分析的效率与判别性，实验显示其在生存预测和分类任务中均取得显著性能提升。
摘要：
全滑片图像（WSIs）通常使用多实例学习（MIL）方法进行分析。然而，WSIs的规模和异质性会产生高度冗余和分散的信息，使得难以识别和整合判别信号。现有MIL方法要么无法有效丢弃无信息的提示，要么在从多个块中整合相关特征方面能力有限，这限制了它们在大规模和异质WSIs上的性能。为了解决这个问题，我们提出了DeltaMIL，一种新的MIL框架，能够显式选择语义相关的区域，并整合WSIs中的判别信息。我们的方法利用门控delta规则，通过块结合遗忘和记忆机制，高效地过滤和整合信息。delta机制通过根据当前块与旧值的相关性动态更新记忆，删除旧值并插入新值。门控机制进一步使无关信号能够快速遗忘。此外，DeltaMIL整合了互补的局部模式混合机制，以保留细粒度的病理局部性。我们的设计增强了有意义提示的提取并抑制冗余或噪声信息，从而提高了模型的鲁棒性和判别能力。实验表明，DeltaMIL实现了最先进的性能。具体而言，对于生存预测，DeltaMIL使用ResNet-50特征提高了3.69%的性能，使用UNI特征提高了2.36%的性能。对于滑片级分类，它使用ResNet-50特征提高了3.09%的准确率，使用UNI特征提高了3.75%的准确率。这些结果展示了DeltaMIL在多样化WSI任务中的强大且一致的性能。
英文摘要：
Whole Slide Images (WSIs) are typically analyzed using multiple instance learning (MIL) methods. However, the scale and heterogeneity of WSIs generate highly redundant and dispersed information, making it difficult to identify and integrate discriminative signals. Existing MIL methods either fail to discard uninformative cues effectively or have limited ability to consolidate relevant features from multiple patches, which restricts their performance on large and heterogeneous WSIs. To address this issue, we propose DeltaMIL, a novel MIL framework that explicitly selects semantically relevant regions and integrates the discriminative information from WSIs. Our method leverages the gated delta rule to efficiently filter and integrate information through a block combining forgetting and memory mechanisms. The delta mechanism dynamically updates the memory by removing old values and inserting new ones according to their correlation with the current patch. The gating mechanism further enables rapid forgetting of irrelevant signals. Additionally, DeltaMIL integrates a complementary local pattern mixing mechanism to retain fine-grained pathological locality. Our design enhances the extraction of meaningful cues and suppresses redundant or noisy information, which improves the model's robustness and discriminative power. Experiments demonstrate that DeltaMIL achieves state-of-the-art performance. Specifically, for survival prediction, DeltaMIL improves performance by 3.69\% using ResNet-50 features and 2.36\% using UNI features. For slide-level classification, it increases accuracy by 3.09\% with ResNet-50 features and 3.75\% with UNI features. These results demonstrate the strong and consistent performance of DeltaMIL across diverse WSI tasks.
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