output
20132026
most citedMitosis domain generalization in histopathology images -- The MIDOG challenge

162 citations

47 papers

cs.CL2026

Location-Aware Language Models via Secondary Embeddings

Gokul Srinivasagan, Munir Georges

Pretrained transformer-based language models achieve strong performance across a wide range of NLP tasks but remain limited in encoding geo-locational semantics, leading to subopti…

cs.HC2026★ 5 cited

Stuck on Suggestions: Automation Bias, the Anchoring Effect, and the Factors That Shape Them in Computational Pathology

Emely Rosbach, Jonas Ammeling, Jonathan Ganz +4

Artificial intelligence (AI)-driven decision support systems can improve diagnostic accuracy and efficiency in computational pathology. However, collaboration between human experts…

cs.LG2025★ 8 cited

Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm

Parthasarathy Nadarajan, Michael Botsch, Sebastian Sardina

In this paper, a probabilistic space-time representation of complex traffic scenarios is predicted using machine learning algorithms. Such a representation is significant for all a…

cs.LG2025★ 9 cited

Probability Estimation for Predicted-Occupancy Grids in Vehicle Safety Applications Based on Machine Learning

Parthasarathy Nadarajan, Michael Botsch

This paper presents a method to predict the evolution of a complex traffic scenario with multiple objects. The current state of the scenario is assumed to be known from sensors and…

cs.CV2025

Benchmarking Foundation Models for Mitotic Figure Classification

Jonas Ammeling, Jonathan Ganz, Emely Rosbach +4

The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled imag…

cs.RO2025

Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors

Tobias Kern, Leon Tolksdorf, Christian Birkner

Physically reduced-scale vehicles are emerging to accelerate the development of advanced automated driving functions. In this paper, we investigate the effects of scaling on self-l…