Exponentially weighted ensemble deep learning for histologic growth pattern classification in lung adenocarcinoma
Year: 2026
Type: preprint
Abstract: Accurate classification of lung adenocarcinoma growth patterns from hematoxylin and eosin (H&E)-stained whole-slide images is essential for treatment planning and prognosis. We develop and evaluate an exponentially weighted ensemble deep learning approach that combines five architectures (EfficientNet-B3, DeiT3, Swin Transformer, Vision Transformer, ConvNeXt) with weights determined by a softmax transformation of per-model Quadratic Weighted Kappa (QWK) scores. Using 25,545 image patches at 40× magnification from the Dartmouth-Hitchcock dataset, we evaluated performance with stratified 5-fold cross-validation. Individual model performance ranged from Cohen’s kappa κ = 0.5563 (Con-vNeXt) to κ = 0.9561 (EfficientNet-B3). The ensemble achieved 96.52% accuracy and κ = 0.9648, exceeding the best individual model by Δ κ = +0.0087 with statistical significance (McNemar’s test, χ 2 = 50.84, p < 10 − 12 ; bootstrap 95% CI on Δ κ [+0.0056, +0.0117]). Pairwise disagreement rates of 16.6–43.2% between architectures indicate that the models capture different aspects of tissue morphology, and the framework automatically down-weights unreliable components, as illustrated by ConvNeXt receiving only 5.7% ensemble weight despite a single-fold training failure. The approach offers a clinically grounded method for ensemble aggregation in digital pathology.
Source: medRxiv
Authors: Rajashekar Korutla, Muskan Naresh Jain, Tejas Mohan Karkera, Anne Breggia, Robert Christman, Stephen T. Ryan, Gene Tunik, Saeed Amal
Institutions: Northeastern University, MaineHealth, Maine Medical Center
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214582587