\begin{abstract}
The histopathological analysis of whole-slide images (WSIs) is fundamental to cancer diagnosis but is a time-consuming and expert-driven process.
While deep learning methods show promising results, dominant patch-based methods artificially fragment tissue, ignore biological boundaries, and produce black-box predictions.
We overcome these limitations with a novel framework that transforms gigapixel WSIs into tissue-boundary aligned graph representations and is interpretable by design.

Our approach builds graph nodes from tissue regions that respect natural structures, not arbitrary grids.
We introduce an adaptive graph coarsening technique, guided by learned embeddings, to efficiently merge homogeneous regions while preserving diagnostically critical details in heterogeneous areas.
Each node is enriched with a compact, interpretable feature set capturing clinically-motivated priors.
A graph attention network then performs diagnosis on this compact representation.

We demonstrate strong performance on cancer staging and survival prediction, outperforming methods with similar data requirements.
Crucially, our data-efficient model (requiring $>300\times$ less training data) achieves results competitive with a massive foundation model, while offering full interpretability through feature attribution.
Our code is publicly available at \url{https://github.com/aweers/pix2pathology}.
\end{abstract}