30 citations · 90 across the 14 of their papers we have counts for
15 papers
Evaluation of Language Models in the Medical Context Under Resource-Constrained Settings
Andrea Posada, Daniel Rueckert, Felix Meissen +1
Since the Transformer architecture emerged, language model development has grown, driven by their promising potential. Releasing these models into production requires properly unde…
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…
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection
Felix Meissen, Johannes Getzner, Alexander Ziller +4
Unsupervised anomaly detection (UAD) alleviates large labeling efforts by training exclusively on unlabeled in-distribution data and detecting outliers as anomalies. Generally, the…
(Predictable) Performance Bias in Unsupervised Anomaly Detection
Felix Meissen, Svenja Breuer, Moritz Knolle +5
Background: With the ever-increasing amount of medical imaging data, the demand for algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models promi…
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…
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
Hongwei Bran Li, Gian Marco Conte, Qingqiao Hu +62
Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input m…