30 citations · 115 across the 15 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…
The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting
Florian Kofler, Felix Meissen, Felix Steinbauer +103
A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition ti…
Robust Detection Outcome: A Metric for Pathology Detection in Medical Images
Felix Meissen, Philip Müller, Georgios Kaissis +1
Detection of pathologies is a fundamental task in medical imaging and the evaluation of algorithms that can perform this task automatically is crucial. However, current object dete…
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
Ioannis Lagogiannis, Felix Meissen, Georgios Kaissis +1
Deep unsupervised approaches are gathering increased attention for applications such as pathology detection and segmentation in medical images since they promise to alleviate the n…
Approaching Peak Ground Truth
Florian Kofler, Johannes Wahle, Ivan Ezhov +15
Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical doma…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Unsupervised Anomaly Localization with Structural Feature-Autoencoders
Felix Meissen, Johannes Paetzold, Georgios Kaissis +1
Unsupervised Anomaly Detection has become a popular method to detect pathologies in medical images as it does not require supervision or labels for training. Most commonly, the ano…
On the Pitfalls of Using the Residual Error as Anomaly Score
Felix Meissen, Benedikt Wiestler, Georgios Kaissis +1
Many current state-of-the-art methods for anomaly localization in medical images rely on calculating a residual image between a potentially anomalous input image and its "healthy"…
AutoSeg -- Steering the Inductive Biases for Automatic Pathology Segmentation
Felix Meissen, Georgios Kaissis, Daniel Rueckert
In medical imaging, un-, semi-, or self-supervised pathology detection is often approached with anomaly- or out-of-distribution detection methods, whose inductive biases are not in…
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI
Felix Meissen, Georgios Kaissis, Daniel Rueckert
In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pa…