ArticleNature cancer2025
SMMILe enables accurate spatial quantification in digital pathology using multiple-instance learning.
Article in Nature cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Explainable AI-Derived Spatial Pathological Features of Tumor, Necrosis, and Lymphocytes Identify Key Histological Signatures for Residual Cancer Burden Assessment in Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- ALPaCA: Adapting Llama for Pathology Context Analysis to enable slide-level question answering.Nature communications · 2026Article
- Spatial architecture of tertiary lymphoid structures represents an independent prognostic dimension in hepatocellular carcinoma.Journal for immunotherapy of cancer · 2026Article
- Attention-guided and MIL-constrained CycleGAN for high-fidelity virtual p16 and Ki-67 staining.Frontiers in medical technology · 2026Article
- Cell stiffness regulates immune evasion during metastatic dormancy.Nature cancer · 2026Article
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Authors and funding
11 authors.
Funding
Abstract
Spatial quantification is a critical step in most computational pathology tasks, from guiding pathologists to areas of clinical interest to discovering tissue phenotypes behind novel biomarkers. To circumvent the need for manual annotations, modern computational pathology methods have favored multiple-instance learning approaches that can accurately predict whole-slide image labels, albeit at the expense of losing their spatial awareness. Here we prove mathematically that a model using instance-level aggregation could achieve superior spatial quantification without compromising on whole-slide image prediction performance. We then introduce a superpatch-based measurable multiple-instance learning method, SMMILe, and evaluate it across 6 cancer types, 3 highly diverse classification tasks and 8 datasets involving 3,850 whole-slide images. We benchmark SMMILe against nine existing methods using two different encoders-an ImageNet pretrained and a pathology-specific foundation model-and show that in all cases SMMILe matches or exceeds state-of-the-art whole-slide image classification performance while simultaneously achieving outstanding spatial quantification.
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Registered trials
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