21 citations · 39 across the 8 of their papers we have counts for
8 papers
CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration
Thomas Buddenkotte, Roland Opfer, Julia Krüger +2
Medical image analysis tasks often focus on regions or structures located in a particular location within the patient's body. Often large parts of the image may not be of interest…
Leveraging the Mahalanobis Distance to enhance Unsupervised Brain MRI Anomaly Detection
Finn Behrendt, Debayan Bhattacharya, Robin Mieling +4
Unsupervised Anomaly Detection (UAD) methods rely on healthy data distributions to identify anomalies as outliers. In brain MRI, a common approach is reconstruction-based UAD, wher…
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection
Finn Behrendt, Debayan Bhattacharya, Lennart Maack +4
Supervised deep learning techniques show promise in medical image analysis. However, they require comprehensive annotated data sets, which poses challenges, particularly for rare d…
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs
Finn Behrendt, Debayan Bhattacharya, Robin Mieling +4
The application of supervised models to clinical screening tasks is challenging due to the need for annotated data for each considered pathology. Unsupervised Anomaly Detection (UA…
Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs
Finn Behrendt, Debayan Bhattacharya, Julia Krüger +2
Radiographs are a versatile diagnostic tool for the detection and assessment of pathologies, for treatment planning or for navigation and localization purposes in clinical interven…
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI
Marcel Bengs, Finn Behrendt, Julia Krüger +2
Purpose. Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis of neurological diseases. Recently, deep learning methods for unsupervised anomaly detection (UAD) h…
Multiple Sclerosis Lesion Activity Segmentation with Attention-Guided Two-Path CNNs
Nils Gessert, Julia Krüger, Roland Opfer +5
Multiple sclerosis is an inflammatory autoimmune demyelinating disease that is characterized by lesions in the central nervous system. Typically, magnetic resonance imaging (MRI) i…
4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation
Nils Gessert, Marcel Bengs, Julia Krüger +5
Multiple sclerosis lesion activity segmentation is the task of detecting new and enlarging lesions that appeared between a baseline and a follow-up brain MRI scan. While deep learn…