Structured State Space Models for Multiple Instance Learning in Digital
  Pathology
Leo Fillioux Joseph Boyd Maria Vakalopoulou Paul-Henry Cournède Stergios Christodoulidis
Multiple instance learning is an ideal mode of analysis for histopathology
data, where vast whole slide images are typically annotated with a single
global label. In such cases, a whole slide image is modelled as a collection of
tissue patches to be aggregated and classified. Common models for performing
this classification include recurrent neural networks and transformers.
Although powerful compression algorithms, such as deep pre-trained neural
networks, are used to reduce the dimensionality of each patch, the sequences
arising from whole slide images remain excessively long, routinely containing
tens of thousands of patches. Structured state space models are an emerging
alternative for sequence modelling, specifically designed for the efficient
modelling of long sequences. These models invoke an optimal projection of an
input sequence into memory units that compress the entire sequence. In this
paper, we propose the use of state space models as a multiple instance learner
to a variety of problems in digital pathology. Across experiments in metastasis
detection, cancer subtyping, mutation classification, and multitask learning,
we demonstrate the competitiveness of this new class of models with existing
state of the art approaches. Our code is available at
https://github.com/MICS-Lab/s4_digital_pathology.