\section{Conclusion and Future Work}
\label{sec:conclusion}

In this work, we explore the notion of self-supervised learning and \ac{UDA} for the identification of histological tissue types. 
Given the difficulty of obtaining expert annotations, we explore diverse \ac{UDA} models in various label-scarce histopathology datasets. 
More importantly, we present a new label transferring approach from a partially labeled source domain to an unlabeled target domain.
This is more practical than most previous \ac{UDA} approaches tailored to fully annotated source domain data and/or tied to additional network branches dedicated to auxiliary tasks. Instead, we perform progressive entropy minimization based on the similarity distribution among the unlabeled target and source domain samples yielding discriminative and domain-agnostic features for domain adaptation.
Through adaptation experiments, we show that our \acl{SRA} method can discover the relevant semantic information even in the presence of few labeled source samples and yields a better generalization on different target domain datasets. 

A future extension of this work is defining a self-supervised learning approach that can embed mixtures of tissues as publicly available datasets are solely composed of homogeneous patches. Such patches are not characteristic of the heterogeneity of complex images present in diagnosis routine and can lead to erroneous detections (e.g., background and stroma interaction interpreted as adipose). Furthermore, the segmentation achieved by our method can be used for clinically relevant applications, such as tumor-stroma ratio calculation, disease-free survival prediction or the adjuvant treatment decision-making.


%Currently, our problem formulation relies on the assumption that, overall, the distributions of the source and target sets present examples of similar classes.
%A future extension of this work would be to explore a more general inductive bias other than the domain alignment, mainly when the target domain contains additional unseen categories.