4 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology
Marie-Lisa Eich, Kai Standvoss, Timo Milbich +30
Lung cancer tissue diagnostics is complex, as therapy decisions in precision oncology rely on the integration of histomorphological, immunohistochemical, and molecular features. Ye…
Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy
Kai Standvoss, Miriam Hägele, Rosemarie Krupar +25
Hematoxylin and eosin (H&E) staining is the cornerstone of histopathology, yet scalable, quantitative analysis of H&E whole-slide images (WSIs) remains a central challenge in compu…
OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
Maaike Galama, Nina Kozar-Gillan, Christina Embacher +18
The tumor microenvironment (TME) plays a central role in cancer progression, treatment response, and patient outcomes, yet large-scale, consistent, and quantitative TME characteriz…
Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
Maximilian Alber, Stephan Tietz, Jonas Dippel +24
Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present Atlas, a novel vision foundati…
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
Marvin Sextro, Gabriel Dernbach, Kai Standvoss +5
Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for…