RoFormer for Position Aware Multiple Instance Learning in Whole Slide
  Image Classification
Etienne Pochet Rami Maroun Roger Trullo
Whole slide image (WSI) classification is a critical task in computational
pathology. However, the gigapixel-size of such images remains a major challenge
for the current state of deep-learning. Current methods rely on
multiple-instance learning (MIL) models with frozen feature extractors. Given
the the high number of instances in each image, MIL methods have long assumed
independence and permutation-invariance of patches, disregarding the tissue
structure and correlation between patches. Recent works started studying this
correlation between instances but the computational workload of such a high
number of tokens remained a limiting factor. In particular, relative position
of patches remains unaddressed. We propose to apply a straightforward encoding
module, namely a RoFormer layer , relying on memory-efficient exact
self-attention and relative positional encoding. This module can perform full
self-attention with relative position encoding on patches of large and
arbitrary shaped WSIs, solving the need for correlation between instances and
spatial modeling of tissues. We demonstrate that our method outperforms
state-of-the-art MIL models on three commonly used public datasets (TCGA-NSCLC,
BRACS and Camelyon16)) on weakly supervised classification tasks. Code is
available at https://github.com/Sanofi-Public/DDS-RoFormerMIL