Histopathological analysis of tissue samples is the cornerstone of cancer diagnosis and treatment but is increasingly strained by rising cancer incidence and a limited number of specialized pathologists~\cite{metter2019Pathologists}.
This growing bottleneck motivates the pressing need for reliable computational methods to assist clinical workflows by screening slides, quantifying morphology, and highlighting diagnostically relevant regions.

Whole Slide Images (WSIs) capture tissue at a gigapixel scale (often exceeding 100,000 × 100,000 pixels) and contain multi-scale morphological cues that are crucial for diagnosis~\cite{Tseng2023HistoGold}.
Modern deep learning pipelines typically handle that scale by partitioning WSIs into small, fixed-size rectangular patches and processing those patches independently~\cite{vanderlaak2021dlinhisto,song2023aiCPath,raciti2023clinicalAIpathImprovement}.
While practical, this patch-based paradigm has two major limitations.
First, rigid patches artificially fragment biological structures (e.g., glands, tumor fronts), destroying natural boundaries and the contextual relationships pathologists use~\cite{ciga2021overcomingPatches}.
Second, many patch-aggregation methods produce predictions that are hard to attribute to clearly delineated regions or interpretable features, which reduces trust and slows clinical adoption.

We address these limitations by introducing a novel framework that transforms WSIs into morphology-aware graphs that are interpretable by design.
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\begin{figure*}[t]
\includegraphics[width=\textwidth,trim={0 0cm 0 0},clip]{figures/HistoGraph7_LL.pdf}
\caption{
Overview of the pipeline. (1-2) Superpixels are segmented and converted to an RA graph. (3-4) Nodes are merged based on embedding similarity, visualized as a merged version of the initial superpixel segmentation (corresp. segmentation). (5-6) Nodes are enriched with interpretable features (texture, morphology, nuclear). (7) GATv2 performs classification. (8) Explainability with respect to regions and features.
}
\label{fig:method}
\end{figure*}
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In this paper, we propose a graph-based framework that converts WSIs into biologically meaningful graphs, where nodes correspond to tissue regions aligned with natural boundaries, edges capture spatial contiguity, and node descriptors comprise a compact, clinically interpretable set of texture, morphological, and nuclear features.
Our contributions are: 
\begin{enumerate}
    \item A novel pipeline (see \figureref{fig:method}) that builds graphs from adaptive tissue regions, preserves structural context, and enriches regions with a compact and interpretable feature vector, enabling clinically relevant explanations.
    \item An embedding-guided graph coarsening strategy that aggregates homogeneous areas for computational efficiency while retaining high granularity in complex regions.
    \item A demonstration that our compact, interpretable model outperforms comparable methods and achieves performance competitive with a large foundation model, while requiring a fraction of the data (300x less) and computational resources.
\end{enumerate}