Multiple instance learning (MIL) has emerged as a powerful framework for weakly supervised whole slide image (WSI) classification, enabling slide-level predictions without requiring detailed patch-level annotations. However, a key limitation of MIL lies in the underexplored potential of pre-training the MIL aggregator. Most existing approaches train it from scratch, resulting in performance heavily dependent on the number of labeled WSIs, while overlooking the abundance of unlabeled WSIs available in real-world scenarios. To address this, we propose PreMix, a novel framework that leverages a non-contrastive pre-training method, Barlow Twins, augmented with the Slide Mixing approach to generate additional positive pairs and enhance feature learning, particularly under limited labeled WSI conditions. Fine-tuning with Mixup and Manifold Mixup further enhances robustness by effectively handling the diverse sizes of gigapixel WSIs. Experimental results demonstrate that integrating HIPT into PreMix achieves an average F1 improvement of 4.7% over the baseline HIPT across various WSI training datasets and label sizes. These findings underscore its potential to advance WSI classification with limited labeled data and its applicability to real-world histopathology practices. The code is available at https://anonymous.4open.science/r/PreMix

翻译：多示例学习（MIL）已成为弱监督全切片图像（WSI）分类的强大框架，能够在无需详细切片级标注的情况下实现玻片级预测。然而，MIL的一个关键局限在于其聚合器的预训练潜力尚未得到充分探索。现有方法大多从头开始训练聚合器，导致性能严重依赖于已标注WSI的数量，同时忽视了现实场景中大量可用的未标注WSI。为解决这一问题，我们提出PreMix——一种新颖的框架，该方法利用非对比预训练方法Barlow Twins，并结合切片混合技术生成额外的正样本对以增强特征学习，尤其在标注WSI有限的条件下。通过结合Mixup与流形Mixup的微调策略，能有效处理千兆像素级WSI的尺寸多样性，从而进一步提升模型鲁棒性。实验结果表明，在不同WSI训练数据集及标注规模下，将HIPT整合至PreMix框架中相比基线HIPT平均F1分数提升了4.7%。这些发现凸显了该方法在有限标注数据下推进WSI分类的潜力及其在现实组织病理学实践中的适用性。代码发布于https://anonymous.4open.science/r/PreMix