In computational pathology, random sampling of patches during training of Multiple Instance Learning (MIL) methods is computationally efficient and serves as a regularization strategy. Despite its promising benefits, questions concerning performance trends for varying sample sizes and its influence on model interpretability remain. Addressing these, we reach an optimal performance enhancement of 1.7% using thirty percent of patches on the CAMELYON16 dataset, and 3.7% with only eight samples on the TUPAC16 dataset. We also find interpretability effects are strongly dataset-dependent, with interpretability impacted on CAMELYON16, while remaining unaffected on TUPAC16. This reinforces that both the performance and interpretability relationships with sampling are closely task-specific. End-to-end training with 1024 samples reveals improvements across both datasets compared to pre-extracted features, further highlighting the potential of this efficient approach.

在计算病理学中，通过对抽样补丁进行训练的多实例学习（MIL）方法在计算效率上是高效的，并且作为一种正则化策略。本研究探讨了不同采样大小对性能趋势以及对模型可解释性的影响，并在CAMELYON16数据集上使用30%的补丁实现了1.7%的性能提升，在TUPAC16数据集上仅使用8个样本实现了3.7%的性能提升。此外，我们发现解释性效果与数据集相关，在CAMELYON16上受到了影响，而在TUPAC16上则没有受到影响，这进一步证实了抽样与性能和解释性之间的关系与任务具体相关。与预先提取的特征相比，使用1024个样本进行的端到端训练在两个数据集上都取得了进一步的改进，进一步突显了这种高效方法的潜力。

基于随机采样的整张切片图像分类的多实例学习