Slot-Mixup with Subsampling: A Simple Regularization for WSI
  Classification
Seongho Keum Sanghyun Kim Soojeong Lee Juho Lee
Whole slide image (WSI) classification requires repetitive zoom-in and out
for pathologists, as only small portions of the slide may be relevant to
detecting cancer. Due to the lack of patch-level labels, multiple instance
learning (MIL) is a common practice for training a WSI classifier. One of the
challenges in MIL for WSIs is the weak supervision coming only from the
slide-level labels, often resulting in severe overfitting. In response,
researchers have considered adopting patch-level augmentation or applying mixup
augmentation, but their applicability remains unverified. Our approach augments
the training dataset by sampling a subset of patches in the WSI without
significantly altering the underlying semantics of the original slides.
Additionally, we introduce an efficient model (Slot-MIL) that organizes patches
into a fixed number of slots, the abstract representation of patches, using an
attention mechanism. We empirically demonstrate that the subsampling
augmentation helps to make more informative slots by restricting the
over-concentration of attention and to improve interpretability. Finally, we
illustrate that combining our attention-based aggregation model with
subsampling and mixup, which has shown limited compatibility in existing MIL
methods, can enhance both generalization and calibration. Our proposed methods
achieve the state-of-the-art performance across various benchmark datasets
including class imbalance and distribution shifts.