  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.
