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.

翻译：在计算病理学中，多实例学习（MIL）方法训练过程中随机采样图像块具有计算效率高和正则化策略的优势。尽管其前景可观，但关于不同采样量下的性能趋势及其对模型可解释性的影响仍存在疑问。针对这些问题，我们在CAMELYON16数据集上使用30%的图像块实现了1.7%的最优性能提升，在TUPAC16数据集上仅使用8个样本就获得了3.7%的提升。我们还发现可解释性效果高度依赖于数据集：在CAMELYON16上可解释性受到影响，而在TUPAC16上则未受影响。这进一步证实了性能与可解释性同采样的关系均高度依赖于具体任务。与预提取特征相比，采用1024个样本的端到端训练在两个数据集上均展现出性能提升，进一步凸显了这种高效方法的潜力。