\textbf{Patch-based Multi Instance Learning (MIL)}. The dominant paradigm in WSI analysis treats slides as bags of fixed-sized patches~\cite{srinidhi2021HistoSurvey,shmatko2022AIinHisto}.
While seminal models like DeepSets~\cite{zaheer2017deepSets}, DSMIL~\cite{li2021dsmil}, and the attention-based ABMIL~\cite{ilse2018abmil} established the effectiveness of this approach, they discard the spatial topology of the tissue.
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Recent foundation models, such as \mbox{UNI2-h}~\cite{chen2024FoundationWSI} and CHIEF~\cite{wang2024CHIEF} scale this approach by pre-training on massive datasets.
However, these methods usually rely on learned latent embeddings that are not interpretable~\cite {kaczmarzyk2024explainable}. While recent efforts like Si-MIL~\cite{kapse2024si} have introduced feature interpretability, they, along with standard MIL, remain constrained by the rigid patch grid, which artificially fragments biological structures such as tumor boundaries.


\textbf{Graph-based WSI Analysis}. To restore spatial context, Graph Neural Networks (GNNs) have been adopted to model the tissue microenvironment, having already demonstrated advantages across various other medical domains~\cite{wu2021GNNSurvey,qiu2024gnnFMri,lux2025interpretableretinal,li2025fine}.
Approaches like Patch-GCN~\cite{chen2021whole} and GraphTransformer~\cite{zheng2022GraphTransformer} construct graphs where nodes are patches and edges represent adjacency. 
Extensions like DM-GNN~\cite{wang2024dmgnn} further refine this by modeling morphological and global dependencies to capture complex patch correlations.
While this improves context modeling, the nodes remain defined by an arbitrary grid, compromising the biological fidelity of the graph structure.

\textbf{Biologically-Aligned Representations}. Moving beyond grids, Cell-Graphs~\cite{zhou2019cgc,pati2022HACT} model individual cells as nodes. While highly granular, this approach faces severe scalability issues, often generating graphs with millions of nodes that are computationally prohibitive for whole-slide analysis. Superpixel methods offer a middle ground by clustering pixels into perceptually meaningful regions~\cite{zormpas2021superhistopath, luo2025selfcalibration}, but typically still rely on latent features that are not interpretable.



\textbf{Our Contribution}. We bridge these gaps by proposing a structure-adaptive graph framework that respects natural tissue boundaries. By employing adaptive coarsening, we efficiently aggregate homogeneous tissue while preserving granularity in heterogeneous regions. Crucially, we utilize interpretable features by design rather than opaque embeddings, ensuring clinical transparency.