  Representation learning of pathology whole-slide images (WSIs) has been has
primarily relied on weak supervision with Multiple Instance Learning (MIL).
However, the slide representations resulting from this approach are highly
tailored to specific clinical tasks, which limits their expressivity and
generalization, particularly in scenarios with limited data. Instead, we
hypothesize that morphological redundancy in tissue can be leveraged to build a
task-agnostic slide representation in an unsupervised fashion. To this end, we
introduce PANTHER, a prototype-based approach rooted in the Gaussian mixture
model that summarizes the set of WSI patches into a much smaller set of
morphological prototypes. Specifically, each patch is assumed to have been
generated from a mixture distribution, where each mixture component represents
a morphological exemplar. Utilizing the estimated mixture parameters, we then
construct a compact slide representation that can be readily used for a wide
range of downstream tasks. By performing an extensive evaluation of PANTHER on
subtyping and survival tasks using 13 datasets, we show that 1) PANTHER
outperforms or is on par with supervised MIL baselines and 2) the analysis of
morphological prototypes brings new qualitative and quantitative insights into
model interpretability.
