13 citations · 28 across the 4 of their papers we have counts for
5 papers
Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
Qian Da, Yijiang Chen, Min Ju +25
Recent breakthroughs in artificial intelligence through foundation models and agents have accelerated the evolution of computational pathology. Demonstrated performance gains repor…
A deep learning framework for efficient pathology image analysis
Peter Neidlinger, Tim Lenz, Sebastian Foersch +24
Artificial intelligence (AI) has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images (WSIs). However, current methods are computa…
Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning
Tim Lenz, Peter Neidlinger, Marta Ligero +3
Representation learning of pathology whole-slide images (WSIs) has primarily relied on weak supervision with Multiple Instance Learning (MIL). This approach leads to slide represen…
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology
Peter Neidlinger, Omar S. M. El Nahhas, Hannah Sophie Muti +13
Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information. However, there is…
End-To-End Clinical Trial Matching with Large Language Models
Dyke Ferber, Lars Hilgers, Isabella C. Wiest +13
Matching cancer patients to clinical trials is essential for advancing treatment and patient care. However, the inconsistent format of medical free text documents and complex trial…