activity
20182020
most citedSkin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting

180 citations · 270 across the 15 of their papers we have counts for

collaborators

33 papers

eess.IV2020

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…

eess.IV2020

4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes

Marcel Bengs, Nils Gessert, Alexander Schlaefer

Tracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high…

eess.IV2020

Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification

Marcel Bengs, Nils Gessert, Wiebke Laffers +6

Early detection of cancerous tissue is crucial for long-term patient survival. In the head and neck region, a typical diagnostic procedure is an endoscopic intervention where a med…

eess.IV20205 cited

Needle tip force estimation by deep learning from raw spectral OCT data

M. Gromniak, N. Gessert, T. Saathoff +1

Purpose. Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside th…

eess.IV20201 cited

A Deep Learning Approach for Motion Forecasting Using 4D OCT Data

Marcel Bengs, Nils Gessert, Alexander Schlaefer

Forecasting motion of a specific target object is a common problem for surgical interventions, e.g. for localization of a target region, guidance for surgical interventions, or mot…

cs.CV20208 cited

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…