Heike Irmgard Grabsch

This paper demonstrates swarm learning for training AI models on decentralized histopathology data to predict molecular features in colorectal cancer.

This study benchmarks AI methods for end-to-end computational pathology, showing ResNet and ViT outperform MIL and CLAM in mutation prediction but not in tumor subtyping.

This paper develops a deep learning method to predict genetic alterations from histological images across multiple cancer types.