Enhancing Lung Cancer Histopathological Subtyping via Fuzzy Patch Scoring Integrated into an Ensemble Deep Learning Framework

Automated histopathological subtyping of lung cancer from stained whole-slide images (WSIs) remains a pivotal yet challenging task due to pronounced tumor heterogeneity, complex cellular morphology, and severe class imbalance in existing datasets. Deep learning models vary in their ability to capture pathomic diversity, and their diagnostic performance is closely tied to the quality of tissue patches extracted from WSIs. To address these challenges, we propose a novel ensemble deep learning framework augmented with fuzzy-weighted patch quality assessment to optimize the selection and weighting of informative regions. High-quality patches are identified using a fuzzy scoring mechanism and processed through multiple pre-trained convolutional neural networks (CNNs) and vision transformer (ViT) models to extract diverse feature representations. These are integrated via latent embeddings, with fuzzy scores incorporated both as auxiliary inputs and as weights in the loss function, reinforcing attention to clinically relevant regions. Our method outperformed current state-of-the-art models by 1.5% and 1.4% (CI: 95%), achieving accuracies of 96.1% on BMIRDS-LUAD and 93.0% on WSSS4LUAD, demonstrating enhanced robustness in subtype classification and strong potential for clinical integration.