From the 1 of 10 linked papers with an AI index.
10 papers
Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy
Kai Standvoss, Miriam Hägele, Rosemarie Krupar +25
The paper introduces Atlas H&E‑TME, an AI system that automatically analyzes H&E‑stained whole‑slide images to predict tissue quality, regions, and cell types across many cancer ty…
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…
Beyond Attention Heatmaps: How to Get Better Explanations for Multiple Instance Learning Models in Histopathology
Mina Jamshidi Idaji, Julius Hense, Tom Neuhäuser +12
Multiple instance learning (MIL) has enabled substantial progress in computational histopathology, where a large amount of patches from gigapixel whole slide images are aggregated…
Investigating the Robustness of Subtask Distillation under Spurious Correlation
Pattarawat Chormai, Klaus-Robert Müller, Grégoire Montavon
Subtask distillation is an emerging paradigm in which compact, specialized models are extracted from large, general-purpose 'foundation models' for deployment in environments with…
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
Florian Bley, Jacob Kauffmann, Simon León Krug +2
Distance-based classifiers, such as k-nearest neighbors and support vector machines, continue to be a workhorse of machine learning, widely used in science and industry. In practic…
Towards Robust Foundation Models for Digital Pathology
Jonah Kömen, Edwin D. de Jong, Julius Hense +9
Biomedical Foundation Models (FMs) are rapidly transforming AI-enabled healthcare research and entering clinical validation. However, their susceptibility to learning non-biologica…