Teamwork makes the dream work? Ensemble learning in computational pathology.

Ensemble learning with five pretrained pathology models improves tumor subtyping, biomarker, and therapy-response prediction in computational pathology.

• Why it matters: This work addresses the need for more accurate and comprehensive analysis methods in pathology, where individual models may have limited capabilities. Enhancing predictive performance can lead to better diagnosis and treatment planning.
• What they did: Luo et al. integrated five pretrained pathology foundation models into a slide-level representation using an ensemble approach, demonstrating the potential of combining multiple models to exploit their complementary information.
• The result: The ensemble approach yielded improvements across tumor subtyping, biomarker, and therapy-response predictions, highlighting the promise of ensemble and multimodal strategies in computational pathology.