Roland Opfer
Orcid: 0000-0002-9911-5478
According to our database1,
Roland Opfer
authored at least 23 papers
between 2006 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2024
BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI.
Int. J. Comput. Assist. Radiol. Surg., September, 2024
Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning.
Comput. Biol. Medicine, 2024
Leveraging the Mahalanobis Distance to Enhance Unsupervised Brain MRI Anomaly Detection.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024
Diffusion Models with Ensembled Structure-Based Anomaly Scoring for Unsupervised Anomaly Detection.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
Abstract: Focused Unsupervised Image Registration for Structure-specific Population Analysis.
Proceedings of the Bildverarbeitung für die Medizin 2024, 2024
2023
Guided Reconstruction with Conditioned Diffusion Models for Unsupervised Anomaly Detection in Brain MRIs.
CoRR, 2023
Proceedings of the Medical Imaging with Deep Learning, 2023
Nodule Detection in Chest Radiographs with Unsupervised Pre-Trained Detection Transformers.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
2022
Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs.
CoRR, 2022
Unsupervised anomaly detection in 3D brain MRI using deep learning with multi-task brain age prediction.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022
Unsupervised Anomaly Detection in 3D Brain MRI Using Deep Learning with Impured Training Data.
Proceedings of the 19th IEEE International Symposium on Biomedical Imaging, 2022
2021
3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI.
CoRR, 2021
Three-dimensional deep learning with spatial erasing for unsupervised anomaly segmentation in brain MRI.
Int. J. Comput. Assist. Radiol. Surg., 2021
2020
Comput. Medical Imaging Graph., 2020
2010
Proceedings of the Medical Imaging 2010: Computer-Aided Diagnosis, 2010
2009
Proceedings of the Medical Imaging 2009: Computer-Aided Diagnosis, 2009
2008
Comparison of computer-aided diagnosis performance and radiologist readings on the LIDC pulmonary nodule dataset.
Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, 2008
Repeatability and noise robustness of spicularity features for computer aided characterization of pulmonary nodules in CT.
Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, 2008
Automatic lesion tracking for a PET/CT based computer aided cancer therapy monitoring system.
Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, 2008
2007
A new general tumor segmentation framework based on radial basis function energy minimization with a validation study on LIDC lung nodules.
Proceedings of the Medical Imaging 2007: Image Processing, 2007
Toward computer-aided emphysema quantification on ultralow-dose CT: reproducibility of ventrodorsal gravity effect measurement and correction.
Proceedings of the Medical Imaging 2007: Computer-Aided Diagnosis, 2007
2006