167 citations · 223 across the 13 of their papers we have counts for
14 papers
Pathology foundation models for training-free case retrieval in salivary gland tumors
Maximilian J. Roesel, Niklas Kiermeyer, Philipp Keyl +1
In pathology, pattern recognition relies on a frame of reference built through extensive training and experience, which may still fall short when encountering rare cancer subtypes.…
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…
Atlas 2 -- Foundation models for clinical deployment
Maximilian Alber, Timo Milbich, Alexandra Carpen-Amarie +24
Pathology foundation models substantially advanced the possibilities in computational pathology --- yet tradeoffs in terms of performance, robustness, and computational requirement…
MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification
David Jacob Drexlin, Jonas Dippel, Julius Hense +4
Deep learning models have made significant advances in histological prediction tasks in recent years. However, for adaptation in clinical practice, their lack of robustness to vary…
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…