21 citations · 43 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
A Modified da Vinci Surgical Instrument for OCE based Elasticity Estimation with Deep Learning
Maximilian Neidhardt, Robin Mieling, Sarah Latus +3
Robot-assisted surgery has advantages compared to conventional laparoscopic procedures, e.g., precise movement of the surgical instruments, improved dexterity, and high-resolution…
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…
A novel optical needle probe for deep learning-based tissue elasticity characterization
Robin Mieling, Johanna Sprenger, Sarah Latus +2
The distinction between malignant and benign tumors is essential to the treatment of cancer. The tissue's elasticity can be used as an indicator for the required tissue characteriz…