2 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
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…
Unsupervised Anomaly Detection in 3D Brain MRI using Deep Learning with Multi-Task Brain Age Prediction
Marcel Bengs, Finn Behrendt, Max-Heinrich Laves +3
Lesion detection in brain Magnetic Resonance Images (MRIs) remains a challenging task. MRIs are typically read and interpreted by domain experts, which is a tedious and time-consum…
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…