\section{Limitations}
\todo{Lengthen limitations again?}
Our method learns a color transformation function that models the preparation and digitization process of a specific scanning pipeline.
This approach is both structure and scale independent, as the mapping operates solely on pixel colors without regard to anatomical features or image resolution.
The method does not address domain differences beyond color, such as variations in texture or structural features introduced by different scanners or sample preparations.
It is designed to close the domain gap through color normalization, by design to prevent hallucinations.

% First, the method assumes that the relative occurrences of semantically related colors are similar in both the source and target domains.
% If the color distributions differ significantly due to variations in tissue types or staining protocols, the learned mapping may fail to generalize and could result in information loss or suboptimal normalization.

% Second, while the model is robust to scale and structure, it is nonsensical to mix data from several scanning pipelines during training.
% Each pipeline introduces its own color characteristics and artifacts.
% If the sets of occurring colors in the mixed domains overlap, which is likely, it is impossible for the model to output different colors based on the inferred input domain.
% , and combining data from multiple pipelines would confound the learning process, leading to an ill-defined color mapping.
% For best results, training should be performed using data from a single, well-characterized pipeline for both source and target domains.

% Finally, the method does not address domain differences beyond color, such as variations in texture or structural features introduced by different scanners or sample preparations.
% It is designed to close the domain gap through color normalization, by design to prevent hallucinations.


\section{Conclusion}
% \todo{
% Direct, unsupervised training on target domain.
% Practically no loss of structure. Despite basically being a non-linear mapping of colors, good matching of target domain color distributions. 
% Color loss and converged discriminator to facility the adversarial training.}

We introduced 1$\times$1 Stainer, a novel method for normalization between histopathology domains.
Our results show that our design decisions are, above all else, effective in mitigating hallucinations and information loss during processing of medical images.
The training procedure is unsupervised and by avoiding a cycle consistency loss, more straightforward than similar state-of-the-art methods.
The proposed color distribution dissimilarity loss leads to models that closely match the color characteristics of the target distribution.
% Our elegant evaluation criterion further reduces the ambiguity of adversarial training  
% and wraps up our proposed, streamlined approach to stain normalization.
Our approach has several desirable properties that facilitate its application.
