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Interpretable Explanations of Black Box Classifiers Applied on Medical Images by Meaningful Perturbations Using Variational Autoencoders
In: (eds.), Bildverarbeitung für die Medizin 2019, Lübeck, Informatik aktuell, Springer Vieweg, Wiesbaden, 197, 2019
Interpretable Explanations of Black Box Classifiers Applied on Medical Images by Meaningful Perturbations using Variational Autoencoders
In: SPIE 10949, Medical Imaging 2019: Image Processing, San Diego, USA, 10949, 1094911-1-1094911-8, 2019
Mouse Model of Cold-Induced Localized Fat Loss (Selective Cryolipolysis)
In: Lasers in Surgery and Medicine, Wiley, 50, 19-20, 2018
Segmentation of subcutaneous fat within mouse skin in 3D OCT image data using random forests
In: SPIE Medical Imaging 2018: Image Processing, Houston, Texas, United States, SPIE, 10574, 1057426-1-1057426-8, 2018
Evaluation verschiedener Ansätze zur 4D-4D-Registrierung kardiologischer MR-Bilddaten
In: (eds.), Bildverarbeitung für die Medizin 2015, Lübeck, Informatik aktuell, Springer, Berlin Heidelberg, 95-100, 2015
Topology-Preserving Shape-Based Regression of Retinal Layers in OCT Image Data Using Convolutional Neural Networks
In: IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venedig, Italien, 1437-1440, 2019
Combined Registration and Segmentation of the Left Ventricle in Cine MR Image Data
In: (eds.), 58. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Lübeck, Abstractband GMDS 2013, ID334:551-552, 2013
Epistemic and Aleatoric Uncertainty Estimation for PED Segmentation in Home OCT Images
In: (eds.), Bildverarbeitung für die Medizin 2022, Heidelberg, Informatik Aktuell, Springer Vieweg, Wiesbaden, 32-37, 2022
Segmentation of Mouse Skin Layers in Optical Coherence Tomography Image Data using Deep Convolutional Neural Networks
Biomedical Optics Express, 10, 7, 3484-3496, 2019
Segmentation of Retinal Low-Cost Optical Coherence Tomography Images Using Deep Learning
In: (eds.), Bildverarbeitung für die Medizin 2020, Berlin, Informatik aktuell, Springer Vieweg, Wiesbaden, 183, 2020
Joint Non-Linear Registration and Level Set Segmentation of the Left Ventricle in 4D MR Image Data
In: (eds.), Student Conference 2013, Medical Engineering Science, Lübeck, Grin Verlag, 27-30, 2013
Registrierung von nicht sichtbaren Laserbehandlungsarealen der Retina in Live-Aufnahmen des Fundus
In: (eds.), Bildverarbeitung für die Medizin 2017, Heidelberg, Informatik aktuell, Springer Vieweg, Berlin Heidelberg, 331-336, 2017
Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning
In: SPIE Medical Imaging 2020, Houston, USA, 2020
Random-Forest-basierte Segmentierung der subkutanen Fettschicht der Mäusehaut in 3D-OCT-Bilddaten
In: (eds.), Bildverarbeitung für die Medizin 2018, Erlangen, Informatik aktuell, Springer Vieweg, Berlin Heidelberg, 203, 2018
Improved SELFF-OCT using motion correction and artifact detection
Investigative Ophthalmology & Visual Science, 62, 8, 2527, 2021
Improved SELFF-OCT using motion correction and artifact detection
Investigative Ophthalmology & Visual Science, 62, 8, 2527, 2021
Segmentation of the dermal-epidermal junction in OCT image data using deep learning
In: (eds.), Student Conference Proceedings 2022: Medical Engineering Science, Medical Informatics, Biomedical Engineering, Auditory Technology, Biophysics and Robotics and Autonomous Systems, Lübeck, Infinite Science Publishing, 219-222, 2022
Joint Multi-Object Registration and Segmentation of Left and Right Cardiac Ventricles in 4D Cine MRI
In: (eds.), SPIE Medical Imaging 2014, Image Processing, San Diego, USA, 9034, 90340M, 2014
Unsupervised Non-correspondence Detection in Medical Images Using an Image Registration Convolutional Neural Network
In: (eds.), Biomedical Image Registration. WBIR 2022, Cham, Lecture Notes in Computer Science, Springer International Publishing, 13386, 3-7, 2022
Image registration and appearance adaptation in non-correspondent image regions for new MS lesions detection
Frontiers in Neuroscience, 16, 2022
Unsupervised Segmentation of Wounds in Optical Coherence Tomography Images Using Invariant Information Clustering
In: (eds.), Bildverarbeitung für die Medizin 2022, Heidelberg, Informatik Aktuell, Springer Vieweg, Wiesbaden, 1-6, 2022
Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies
International Journal of Computer Assisted Radiology and Surgery, 2022
CNN-based joint non-correspondence detection and registration of retinal optical coherence tomography images
In: CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition, June 21-25, 2021, Munich, Germany, International Journal of Computer Assisted Radiology and Surgery, 16, 20-21, 2021
English
DeutschMedizinische Informatik
an der Uni Lübeck studieren
Institutssekretariat
Susanne Petersen
Tel+49 451 3101 5601
Fax+49 451 3101 5604
Gebäude 64 (Informatik)
Ratzeburger Allee 160
23538 Lübeck
Deutschland
Susanne Petersen
Tel+49 451 3101 5601
Fax+49 451 3101 5604
Gebäude 64 (Informatik)
Ratzeburger Allee 160
23538 Lübeck
Deutschland