\begin{abstract}
Histological staining is a crucial step in the analysis of tissue samples, enabling pathologists to identify and diagnose diseases. However, variations in staining protocols and equipment can lead to inconsistencies in image quality. 
As a result, histological stain normalization remains an active area of research with increasing demand for AI driven diagnosis support.
Unlike other domain adaptation problems, in pathology, the consequences of image hallucinations, obscuring or inserting information, are much greater.
We propose a method that mitigates the risk of hallucinations by simplifying the network architecture and training process.
\rebutextra{Our fully $1\times1$ convolutional architecture prevents textural modifications and we show that a residual connection combined with weight regularization effectively suppresses color information loss.}
The model does not need supervision from CycleGAN based models.
% They are trained directly and leverage an upper bound on the earth mover's distance of the color distributions for better convergence without requiring any paired images.
It is trained directly and leverages target domain color distribution information for better convergence without requiring any paired images.
% As a result, our method needs fewer images and converges to better conditioned functions, retaining more of the input information in its outputs.
As a result, our method can be trained concurrently on images at varying scales, showing differing anatomical structures or dyes.
This simplifies the dataset collection, facilitates the adoption at new centers, and reduces the number of models needed at inference.
\end{abstract}
