\section{Conclusion}
\label{sec:conclusion}
In this work, we have developed PrPSeg, a token-based dynamic segmentation network, specifically crafted to facilitate panoramic renal pathology segmentation by effectively modeling the spatial interconnections among diverse anatomical structures. This innovative approach enables the consistent use of the same architectural framework amidst dataset expansions and introduces a universal proposition matrix. This matrix adeptly transforms intricate anatomical relationships into computational modeling paradigms. Furthermore, we have introduced a novel anatomical loss function, integrating these spatial relationships into our model's training regimen through semi-supervised learning. To the best of our knowledge, our algorithm is the first to achieve comprehensive panoramic segmentation in the domain of renal pathology. The integration of token-based dynamic head design alongside our universal proposition learning strategy, which meticulously maps anatomical relationships into the realm of engineering programming, enhances the model’s efficacy in all-encompassing segmentation. 

\section{Acknowledgement}
This research was supported by NIH R01DK135597(Huo), DoD HT9425-23-1-0003(HCY), NIH NIDDK DK56942(ABF). This work was also supported by Vanderbilt Seed Success Grant, Vanderbilt Discovery Grant, and VISE Seed Grant. This research was also supported by NIH grants R01EB033385, R01DK132338, REB017230, R01MH125931, and NSF 2040462. We extend gratitude to NVIDIA for their support by means of the NVIDIA hardware grant. 