Abstract
Identifying cell types and subtypes from routine histopathology images is essential for improving the computational understanding of human disease. Existing tile-based models can capture detailed nuclear morphology but often fail to incorporate the broader tissue context that influences a cell's function and identity. In addition, available human annotations are typically coarse-grained and unevenly distributed across studies, making fine-grained subtype-level supervision difficult to obtain. To address these limitations, we introduce NuClass, a pathologist workflow inspired framework for cell-wise multi-scale integration of nuclear morphology and microenvironmental context. NuClass includes two main components: Path local, which focuses on nuclear morphology from 224-by-224 pixel crops, and Path global, which models the surrounding 1024-by-1024 pixel neighborhood. A learnable gating module adaptively balances local detail and contextual cues. To encourage complementary learning, we incorporate an uncertainty-guided objective that directs the global path to prioritize regions where the local path is uncertain. We also provide calibrated confidence estimates and Grad-CAM visualizations to enhance interpretability. To overcome the lack of high-quality annotations, we construct a marker-guided dataset from Xenium spatial transcriptomics assays, yielding single-cell resolution labels for more than two million cells across eight organs and 16 classes. Evaluated on three fully held-out cohorts, NuClass achieves up to 96 percent F1 for its best-performing class, outperforming strong baselines. Our results show that multi-scale, uncertainty-aware fusion can bridge the gap between slide-level pathological foundation models and reliable, cell-level phenotype prediction.
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URL
https://arxiv.org/abs/2511.13586
