5 papers
In-Context Multiple Instance Learning
Alexander Möllers, Marvin Sextro, Julius Hense +2
Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from comput…
MapPFN: Learning Causal Perturbation Maps in Context
Marvin Sextro, Weronika KÅos, Gabriel Dernbach
Planning effective interventions in biological systems requires treatment-effect models that adapt to unseen biological contexts by identifying their specific underlying mechanisms…
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…
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…