LENS: Learnable Edge Network Sparsification for Interpretable Histopathology

Graph neural networks for whole-slide histopathology typically operate on dense spatial graphs that mix diagnostically relevant tissue interfaces with many uninformative connections, increasing compute and obscuring the tissue relationships used for prediction. We introduce LENS, a weakly supervised framework for task-driven per-slide topology learning: from slide-level labels alone, Graph neural networks for whole-slide histopathology typically operate on dense spatial graphs that mix diagnostically relevant tissue interfaces with many uninformative connections, increasing compute and obscuring the tissue relationships used for prediction. We introduce **LENS**, a weakly supervised framework for task-driven per-slide topology learning: from slide-level labels alone, **LENS** learns which tissue-patch edges to retain and freezes them at inference into a deterministic sparse adjacency. This sparse adjacency serves as both the computation substrate and a structural explanation, rather than a post-hoc overlay on a dense model. Across 5,530 slides spanning lung, kidney, and breast cancer using three encoders (**SimCLR**, **CTransPath**, **UNI2-h**), **LENS**/**LENS+** achieve the most favorable accuracy-sparsity-compute trade-off: they remove  72% of candidate edges while **LENS+** attains best or second-best accuracy on all four benchmarks, with up to 6.3x lower compute than the strongest dense graph baseline. Because **LENS** heatmaps are derived directly from the sparse graph used in the forward pass, they provide structural rather than post-hoc explanations: in a blinded, head-to-head pathologist evaluation against attention-based MIL and dense-graph attribution baselines, **LENS** achieves 97% RoI precision and 92% recall (F1 = 0.93), with at least 73% fewer false positives than these baselines, and 78% of retained edges are concentrated in tumor and tumor-stroma regions without any region-level supervision. Task-supervised topology learning offers a principled mechanism for jointly optimizing efficiency, accuracy, and interpretability in computational pathology. Code and models: https://github.com/LENS-network-ai/LENS.