180 citations · 630 across the 49 of their papers we have counts for
9 papers · 1 filter
Unsupervised Anomaly Detection of Paranasal Anomalies in the Maxillary Sinus
Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker +8
Deep learning (DL) algorithms can be used to automate paranasal anomaly detection from Magnetic Resonance Imaging (MRI). However, previous works relied on supervised learning techn…
Supervised Contrastive Learning to Classify Paranasal Anomalies in the Maxillary Sinus
Debayan Bhattacharya, Benjamin Tobias Becker, Finn Behrendt +10
Using deep learning techniques, anomalies in the paranasal sinus system can be detected automatically in MRI images and can be further analyzed and classified based on their volume…
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…
Ultrasound Shear Wave Elasticity Imaging with Spatio-Temporal Deep Learning
Maximilian Neidhardt, Marcel Bengs, Sarah Latus +4
Ultrasound shear wave elasticity imaging is a valuable tool for quantifying the elastic properties of tissue. Typically, the shear wave velocity is derived and mapped to an elastic…
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with impured training data
Finn Behrendt, Marcel Bengs, Frederik Rogge +3
The detection of lesions in magnetic resonance imaging (MRI)-scans of human brains remains challenging, time-consuming and error-prone. Recently, unsupervised anomaly detection (UA…
Modeling R Needle Steering in Uppaal
Sascha Lehmann, Antje Rogalla, Maximilian Neidhardt +3
Medical cyber-physical systems are safety-critical, and as such, require ongoing verification of their correct behavior, as system failure during run time may cause severe (or even…