Multiscale kernels. - Roland Opfer (Adv. Comput. Math., 2006)

Toward computer-aided emphysema quantification on ultralow-dose CT: reproducibility of ventrodorsal gravity effect measurement and correction. - Rafael Wiemker; Roland Opfer; Thomas Bülow; Patrik Rogalla; Amnon Steinberg; Ekta Dharaiya; Krishna Subramanyan (Computer-Aided Diagnosis, 2007)

A new general tumor segmentation framework based on radial basis function energy minimization with a validation study on LIDC lung nodules. - Roland Opfer; Rafael Wiemker (Image Processing, 2007)

Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system. - Roland Opfer; Winfried Brenner; Ingwer Carlsen; Steffen Renisch; Jörg Sabczynski; Rafael Wiemker (Computer-Aided Diagnosis, 2008)

Repeatability and noise robustness of spicularity features for computer aided characterization of pulmonary nodules in CT. - Rafael Wiemker; Roland Opfer; Thomas Bülow; Sven Kabus; Ekta Dharaiya (Computer-Aided Diagnosis, 2008)

Comparison of computer-aided diagnosis performance and radiologist readings on the LIDC pulmonary nodule dataset. - Luyin Zhao; Michael C. Lee; Lilla Böröczky; Victor Vloemans; Roland Opfer (Computer-Aided Diagnosis, 2008)

Follow-up segmentation of lung tumors in PET and CT data. - Roland Opfer; Sven Kabus; Torben Schneider; Ingwer C. Carlsen; Steffen Renisch; Jörg Sabczynski (Computer-Aided Diagnosis, 2009)

Towards automatic determination of total tumor burden from PET images. - Steffen Renisch; Roland Opfer; Rafael Wiemker (Computer-Aided Diagnosis, 2010)

Multiple sclerosis lesion activity segmentation with attention-guided two-path CNNs. - Nils Gessert; Julia Krüger; Roland Opfer; Ann-Christin Ostwaldt; Praveena Manogaran; Hagen H. Kitzler; Sven Schippling; Alexander Schlaefer (Comput. Medical Imaging Graph., 2020)

4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation. - Nils Gessert; Marcel Bengs; Julia Krüger; Roland Opfer; Ann-Christin Ostwaldt; Praveena Manogaran; Sven Schippling; Alexander Schlaefer (CoRR, 2020)

Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI. - Marcel Bengs; Finn Behrendt; Julia Krüger; Roland Opfer; Alexander Schlaefer (Int. J. Comput. Assist. Radiol. Surg., 2021)

3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI. - Marcel Bengs; Finn Behrendt; Julia Krüger; Roland Opfer; Alexander Schlaefer (CoRR, 2021)

Unsupervised Anomaly Detection in 3D Brain MRI Using Deep Learning with Impured Training Data. - Finn Behrendt; Marcel Bengs; Frederik Rogge; Julia Krüger; Roland Opfer; Alexander Schlaefer (ISBI, 2022)

Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction. - Marcel Bengs; Finn Behrendt; Max-Heinrich Laves; Julia Krüger; Roland Opfer; Alexander Schlaefer (Computer-Aided Diagnosis, 2022)

Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs. - Finn Behrendt; Debayan Bhattacharya; Julia Krüger; Roland Opfer; Alexander Schlaefer (CoRR, 2022)

Nodule Detection in Chest Radiographs with Unsupervised Pre-Trained Detection Transformers. - Finn Behrendt; Debayan Bhattacharya; Julia Krüger; Roland Opfer; Alexander Schlaefer (ISBI, 2023)

Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI. - Finn Behrendt; Debayan Bhattacharya; Julia Krüger; Roland Opfer; Alexander Schlaefer (MIDL, 2023)

Abstract: Focused Unsupervised Image Registration for Structure-specific Population Analysis. - Jan Ehrhardt; Hristina Uzunova; Paul Kaftan; Julia Krüger; Roland Opfer; Heinz Handels (Bildverarbeitung für die Medizin, 2024)

Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection. - Finn Behrendt; Debayan Bhattacharya; Lennart Maack; Julia Krüger; Roland Opfer; Robin Mieling; Alexander Schlaefer (ISBI, 2024)

Leveraging the Mahalanobis Distance to Enhance Unsupervised Brain MRI Anomaly Detection. - Finn Behrendt; Debayan Bhattacharya; Robin Mieling; Lennart Maack; Julia Krüger; Roland Opfer; Alexander Schlaefer (MICCAI (11), 2024)

Combining Reconstruction-based Unsupervised Anomaly Detection with Supervised Segmentation for Brain MRIs. - Finn Behrendt; Debayan Bhattacharya; Lennart Maack; Julia Krüger; Roland Opfer; Alexander Schlaefer (MIDL, 2024)

Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning. - Roland Opfer; Tjalf Ziemssen; Julia Krüger; Thomas Buddenkotte; Lothar Spies; Carola Gocke; Matthias Schwab; Ralph Buchert (Comput. Biol. Medicine, 2024)

CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration. - Thomas Buddenkotte; Roland Opfer; Julia Krüger; Alessa Hering; Mireia Crispin-Ortuzar (CoRR, 2024)

BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI. - Roland Opfer; Julia Krüger; Thomas Buddenkotte; Lothar Spies; Finn Behrendt; Sven Schippling; Ralph Buchert (Int. J. Comput. Assist. Radiol. Surg., 2024)

Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs. - Finn Behrendt; Debayan Bhattacharya; Robin Mieling; Lennart Maack; Julia Krüger; Roland Opfer; Alexander Schlaefer (Comput. Biol. Medicine, 2025)