面向计算病理学知识蒸馏的动量记忆
Momentum Memory for Knowledge Distillation in Computational Pathology
用动量更新的跨批次记忆库替代批次内对齐，把基因组监督蒸馏进纯组织学推理模型，并解耦双分支梯度；这套「记忆库扩大监督上下文 + 梯度解耦」的做法可直接迁移到其他缺失模态的多模态蒸馏场景（如影像-文本、影像-临床指标）。
原文依据 a cross-modal distillation framework driven by a momentum-updated memory
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论融合基因组学与组织病理学的多模态学习在癌症诊断中展现出强大潜力，但其临床转化受限于配对的组织学-基因组学数据稀缺。知识蒸馏通过将基因组监督迁移到组织病理学模型中，使仅凭组织学即可完成准确推理，从而提供了一种实用的解决方案。然而，现有知识蒸馏方法依赖批次内局部对齐，受限于批次内比较数量有限而引入不稳定性，最终导致性能下降。为解决上述局限，我们提出动量记忆知识蒸馏（MoMKD），一种由动量更新记忆驱动的跨模态蒸馏框架。该记忆跨批次聚合基因组与组织病理学信息，有效扩大了每个小批次可用的监督上下文。此外，我们解耦了基因组分支与组织学分支的梯度，防止训练过程中基因组信号主导组织学特征学习，并消除推理时的模态鸿沟问题。在 TCGA-BRCA 基准（HER2、PR 与 ODX 分类任务）以及一个独立自建测试数据集上的大量实验表明，MoMKD 持续优于最优的 MIL 与多模态知识蒸馏基线，在仅组织学推理下取得强劲的性能与泛化能力。总体而言，MoMKD 为计算病理学建立了一种稳健且可泛化的知识蒸馏范式。
英文原文摘要 1518 字符
Multimodal learning that integrates genomics and histopathology has shown strong potential in cancer diagnosis, yet its clinical translation is hindered by the limited availability of paired histology-genomics data. Knowledge distillation (KD) offers a practical solution by transferring genomic supervision into histopathology models, enabling accurate inference using histology alone. However, existing KD methods rely on batch-local alignment, which introduces instability due to limited within-batch comparisons and ultimately degrades performance.To address these limitations, we propose Momentum Memory Knowledge Distillation (MoMKD), a cross-modal distillation framework driven by a momentum-updated memory. This memory aggregates genomic and histopathology information across batches, effectively enlarging the supervisory context available to each mini-batch. Furthermore, we decouple the gradients of the genomics and histology branches, preventing genomic signals from dominating histology feature learning during training and eliminating the modality-gap issue at inference time.Extensive experiments on the TCGA-BRCA benchmark (HER2, PR, and ODX classification tasks) and an independent in-house testing dataset demonstrate that MoMKD consistently outperforms state-of-the-art MIL and multimodal KD baselines, delivering strong performance and generalization under histology-only inference. Overall, MoMKD establishes a robust and generalizable knowledge distillation paradigm for computational pathology.