14 citations · 15 across the 3 of their papers we have counts for
5 papers · 1 filter
ChEX: Interactive Localization and Region Description in Chest X-rays
Philip Müller, Georgios Kaissis, Daniel Rueckert
Report generation models offer fine-grained textual interpretations of medical images like chest X-rays, yet they often lack interactivity (i.e. the ability to steer the generation…
Language Models Meet Anomaly Detection for Better Interpretability and Generalizability
Jun Li, Su Hwan Kim, Philip Müller +5
This research explores the integration of language models and unsupervised anomaly detection in medical imaging, addressing two key questions: (1) Can language models enhance the i…
Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
Philip Müller, Felix Meissen, Georgios Kaissis +1
Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision. The successe…
Anatomy-Driven Pathology Detection on Chest X-rays
Philip Müller, Felix Meissen, Johannes Brandt +2
Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologist…
Rotation-Equivariant Deep Learning for Diffusion MRI
Philip Müller, Vladimir Golkov, Valentina Tomassini +1
Convolutional networks are successful, but they have recently been outperformed by new neural networks that are equivariant under rotations and translations. These new networks wor…