Ensemble transformer-based multiple instance learning for predicting neoadjuvant chemotherapy response from breast cancer biopsy whole-slide images

By relying solely on hematoxylin and eosin-stained WSIs, the framework provides a fully automated, interpretable, and resource-efficient approach suitable for real-world deployment. The two-stage classification design offers fine-grained stratification between pCR, partial, and poor responders, which is critical for personalized therapy planning. Future work will focus on expanding multi-center datasets and integrating advanced pathology foundation models to further enhance cross-domain generalization and clinical applicability.