解决病理图像训练中数据冗余与标注稀缺难题，提升效率与精度。
Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Framework
- 将不同长度的病理特征打包成固定长度，支持批量训练。
- 通过残差分支融合多张切片特征，提升监督信号利用率。
- 采用注意力下采样减少冗余，训练速度提升88%，准确率增8%。
计算病理学将病理切片数字化为全切片图像（WSIs），支持癌症诊断与预后分析。然而，WSIs序列长度极长（最高达20万）、跨度大（200至20万），且标注稀疏。这种极端长度差异导致数据异质性高、冗余严重。传统方法在有限标注下常牺牲训练效率或优化效果以保留异质性。为此，本文提出基于打包的多实例学习框架（PackMIL）：将多个采样的可变长度特征序列打包为固定长度，实现批处理训练的同时保持数据多样性；引入残差分支，将多张切片被丢弃的特征整合为超切片，并赋予定制标签，提供多切片监督并缓解采样损失；同时设计注意力驱动的下采样器，压缩双分支特征以降低冗余。在PANDA(UNI)数据集上，该方法仅用12%的训练时间，准确率提升最高达8%。大量实验表明，聚焦计算病理中的数据挑战，在基础模型时代具有巨大潜力。代码已开源：https://github.com/FangHeng/PackMIL。
原文摘要 · Abstract (English)
Computational pathology (CPath) digitizes pathology slides into whole slide images (WSIs), enabling analysis for critical healthcare tasks such as cancer diagnosis and prognosis. However, WSIs possess extremely long sequence lengths (up to 200K), significant length variations (from 200 to 200K), and limited supervision. These extreme variations in sequence length lead to high data heterogeneity and redundancy. Conventional methods often compromise on training efficiency and optimization to preserve such heterogeneity under limited supervision. To comprehensively address these challenges, we propose a pack-based MIL framework. It packs multiple sampled, variable-length feature sequences into fixed-length ones, enabling batched training while preserving data heterogeneity. Moreover, we introduce a residual branch that composes discarded features from multiple slides into a hyperslide which is trained with tailored labels. It offers multi-slide supervision while mitigating feature loss from sampling. Meanwhile, an attention-driven downsampler is introduced to compress features in both branches to reduce redundancy. By alleviating these challenges, our approach achieves an accuracy improvement of up to 8% while using only 12% of the training time in the PANDA(UNI). Extensive experiments demonstrate that focusing data challenges in CPath holds significant potential in the era of foundation models. The code is https://github.com/FangHeng/PackMIL
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