8 citations · 8 across the 5 of their papers we have counts for
5 papers
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…
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…