SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosis
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
Spread through air spaces (STAS) represents a newly identified aggressive
pattern in lung cancer, which is known to be associated with adverse prognostic
factors and complex pathological features. Pathologists currently rely on time
consuming manual assessments, which are highly subjective and prone to
variation. This highlights the urgent need for automated and precise diag
nostic solutions. 2,970 lung cancer tissue slides are comprised from multiple
centers, re-diagnosed them, and constructed and publicly released three lung
cancer STAS datasets: STAS CSU (hospital), STAS TCGA, and STAS CPTAC. All STAS
datasets provide corresponding pathological feature diagnoses and related
clinical data. To address the bias, sparse and heterogeneous nature of STAS, we
propose an scale-aware multiple instance learning(SMILE) method for STAS
diagnosis of lung cancer. By introducing a scale-adaptive attention mechanism,
the SMILE can adaptively adjust high attention instances, reducing
over-reliance on local regions and promoting consistent detection of STAS
lesions. Extensive experiments show that SMILE achieved competitive diagnostic
results on STAS CSU, diagnosing 251 and 319 STAS samples in CPTAC
andTCGA,respectively, surpassing clinical average AUC. The 11 open baseline
results are the first to be established for STAS research, laying the
foundation for the future expansion, interpretability, and clinical integration
of computational pathology technologies. The datasets and code are available at
https://anonymous.4open.science/r/IJCAI25-1DA1.
