通过全局上下文嵌入提升病理切片分析，显著降低模型复杂度。
CAPRMIL: Context-Aware Patch Representations for Multiple Instance Learning
- 用全局上下文令牌生成上下文感知的图像块表示，避免复杂注意力聚合。
- 在多个公开数据集上达到顶尖性能，参数量减少48%-92.8%。
- 适合资源受限场景，推理速度更快，内存占用更低，适合实际部署。
在计算病理学中，由于全切片图像（WSIs）的吉字节级规模和像素级标注稀缺，弱监督已成为深度学习的标准方法，多实例学习（MIL）成为滑片级模型训练的主要框架。本文受神经偏微分方程求解器启发，提出一种新型MIL设置：不依赖复杂的注意力聚合机制，而是设计一种高效、与聚合器无关的框架，将相关性学习从聚合器中移除。CAPRMIL通过将冻结的图像块编码器提取的特征投影到一组少量全局上下文/形态感知令牌中，并利用多头自注意力机制，以线性复杂度注入全局上下文信息。结合简单的均值MIL聚合器，该方法在多个公开病理学基准上达到当前最优滑片级性能，同时相比主流MIL模型，可减少48%–92.8%的可训练参数，推理阶段减少52%–99%的浮点运算量，并在GPU内存效率和训练时间上位居前列。结果表明，在聚合前学习丰富且上下文感知的实例表示，是全切片分析的一种有效且可扩展的替代方案。代码已开源：https://github.com/mandlos/CAPRMIL。
原文摘要 · Abstract (English)
In computational pathology, weak supervision has become the standard for deep learning due to the gigapixel scale of WSIs and the scarcity of pixel-level annotations, with Multiple Instance Learning (MIL) established as the principal framework for slide-level model training. In this paper, we introduce a novel setting for MIL methods, inspired by proceedings in Neural Partial Differential Equation (PDE) Solvers. Instead of relying on complex attention-based aggregation, we propose an efficient, aggregator-agnostic framework that removes the complexity of correlation learning from the MIL aggregator. CAPRMIL produces rich context-aware patch embeddings that promote effective correlation learning on downstream tasks. By projecting patch features -- extracted using a frozen patch encoder -- into a small set of global context/morphology-aware tokens and utilizing multi-head self-attention, CAPRMIL injects global context with linear computational complexity with respect to the bag size. Paired with a simple Mean MIL aggregator, CAPRMIL matches state-of-the-art slide-level performance across multiple public pathology benchmarks, while reducing the total number of trainable parameters by 48%-92.8% versus SOTA MILs, lowering FLOPs during inference by 52%-99%, and ranking among the best models on GPU memory efficiency and training time. Our results indicate that learning rich, context-aware instance representations before aggregation is an effective and scalable alternative to complex pooling for whole-slide analysis. Our code is available at https://github.com/mandlos/CAPRMIL
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