P = Poster, S = Softwaredemonstration
Die Nummerierung der Poster ist systematisch innerhalb der Postersessions. Um unterschiedlichen Interessen gerecht zu werden, sind innerhalb jeder Postersession mehrere inhaltliche Kategorien vertreten.
Visible Light
| P01: Rotation Invariance for Unsupervised Cell Representation Learning: Analysis of The Impact of Enforcing Rotation Invariance or Equivariance on Representation for Cell Classification Philipp Gräbel et al. Rheinisch-Westfälische Technische Hochschule Aachen, Institute of Imaging & Computer Vision |
| P02: Abstract: Deep Learning-based Quantification of Pulmonary Hemosiderophages in Cytology Slides Christian Marzahl et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P18: Reduction of stain variability in bone marrow microscopy imagesReduction of Stain Variability in Bone Marrow Microscopy Images: Influence of Augmentation and Normalization Methods on Detection and Classification of Hematopoietic Cells Philipp Gräbel et al. Rheinisch-Westfälische Technische Hochschule Aachen, Institute of Imaging & Computer Vision |
| P19: Cell Detection for Asthma on Partially Annotated Whole Slide Images: Learning to be EXACT Christian Marzahl et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P35: Abstract: Deep Learning Algorithms Out-perform Veterinary Pathologists in Detecting the Mitotically Most Active Tumor Region Marc Aubreville et al. Technische Hochschule Ingolstadt, Computer Science |
Imaging and Image Reconstruction
| P03: Learning the Inverse Weighted Radon Transform Philipp Roser et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P04: Table Motion Detection in Interventional Coronary Angiography Junaid R. Rajput et al. Siemens Healthcare GmbH, Advanced Therapies - Innovation |
| P05: Semi-permeable Filters for Interior Region of Interest Dose Reduction in X-ray Microscopy Yixing Huang et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P20: Combining Reconstruction and Edge Detection in Computed Tomography Jürgen Frikel et al. Ostbayerische Technische Hochschule Regensburg |
| P21: 2D Respiration Navigation Framework for 3D Continuous Cardiac Magnetic Resonance Imaging Elisabeth Hoppe et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P22: Residual Neural Network for Filter Kernel Design in Filtered Back-projection for CT Image Reconstruction Jintian Xu et al. Shanghai Jiao Tong University |
| P36: Abstract: Maximum A-posteriori Signal Recovery for OCT Angiography Image Generation Lennart Husvogt et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P37: Abstract: Simultaneous Estimation of X-ray Back-scatter and Forward-scatter using Multi-task Learning Philipp Roser et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
Computer-assisted Intervention
| P06: An Optical Colon Contour Tracking System for Robotaided Colonoscopy: Localization of a Balloon in an Image using the Hough-transform Giuliano Giacoppo et al. Universität Stuttgart, Institut für Medizingerätetechnik |
| P07: Externe Ventrikeldrainage mittels Augmented Reality und Peer-to-Peer-Navigation Simon Strzeletz et al. Hochschule Offenburg, Labor für Computerassistierte Medizin |
| P23: Abstract: Automatic Plane Adjustment in Surgical Cone Beam CT-volumes Celia Martin Vicario et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P24: Abstract: Towards Automatic C-arm Positioning for Standard Projections in Orthopedic Surgery Lisa Kausch et al. Deutsches Krebsforschungszentrum, Medical Image Computing |
Computer-aided operation planning
| P08: Abstract: Contour-based Bone Axis Detection for X-ray-guided Surgery on the Knee Florian Kordon et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P25: Open-Science Gefäßphantom für neurovaskuläre Interventionen Lena Stevanovic et al. Technische Hochschule Ulm, Institut für Medizintechnik und Mechatronik |
Segmentation
| P09: Novel Evaluation Metrics for Vascular Structure Segmentation Marcel Reimann et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P10: A Machine Learning Approach Towards Fatty Liver Disease Detection In Liver Ultrasound Images Adarsh Kuzhipathalil et al. Otto von Guericke Universität Magdeburg, Faculty of Computer Science |
| P11: Deep Learning-based Segmentation of Brain, SEEG and DBS Electrodes on CT Images Vanja Vlasov et al. Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, Luxembourg |
| P26: Abstract: Semi-supervised Segmentation Based on Errorcorrecting Supervision Robert Mendel et al. Ostbayerische Technische Hochschule Regensburg, Regensburg Medical Image Computing |
| P27: Abstract: Efficient Biomedical Image Segmentation on EdgeTPUs Andreas M. Kist et al. University Hospital Erlangen, Phoniatrics and Pediatric Audiology |
| P38: Deep Learning-based Spine Centerline Extraction in Fetal Ultrasound Astrid Franz et al. Philips GmbH, Innovative Technologies |
| P39: Abstract: Studying Robustness of Semantic Segmentation under Domain Shift in Cardiac MRI Peter M. Full et al. Deutsches Krebsforschungszentrum, Medical Image Computing |
| P40: On Efficient Extraction of Pelvis Region from CT Data Tatyana Ivanovska et al. Georg-August University Göttingen, Department for Computational Neuroscience |
U-Net Applications
| P12: Segmentation of the Fascia Lata in Magnetic Resonance Images of the Thigh: Comparison of an Unsupervised Technique with a U-Net in 2D and Patch-wise 3D Lis J. Louise et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Institute of Medical Physics |
| P28: Human Axon Radii Estimation at MRI Scale: Deep Learning Combined with Large-scale Light Microscopy Laurin Mordhorst et al. Universitätsklinikum Hamburg-Eppendorf, Institute of Systems Neuroscience |
| P41: CT Normalization by Paired Image-to-image Translation for Lung Emphysema Quantification Insa Lange et al. Universität zu Lübeck, Institut für Medizinische Informatik |
Attention Maps
| P42: Ultrasound Breast Lesion Detection using Extracted Attention Maps from a Weakly Supervised Convolutional Neural Network Dalia Rodríguez-Salas et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| S02: M3d-CAM: A PyTorch Library to Generate 3D Attention Maps for Medical Deep Learning Karol Gotkowski et al. Technische Universität Darmstadt und Fraunhofer IGD |
Computer-Aided Diagnosis
| P13: Abstract: Automatic CAD-RADS Scoring using Deep Learning Felix Denzinger et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P14: Towards Deep Learning-based Wall Shear Stress Prediction for Intracranial Aneurysms Annika Niemann et al. Otto von Guericke Universität Magdeburg |
| P29: Age Estimation on Panoramic Dental X-ray Images using Deep Learning Sarah Wallraff et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P43: Abstract: Extracting and Leveraging Nodule Features with Lung Inpainting for Local Feature Augmentation Sebastian Gündel et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P44: Abstract: Automatic Dementia Screening and Scoring by Applying Deep Learning on Clock-drawing Tests Shuqing Chen et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| S03: Coronary Plaque Analysis for CT Angiography Clinical Research Felix Denzinger et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
Registration
| P15: Evaluating Design Choices for Deep Learning Registration Networks: Architecture Matters Hanna Siebert et al. Universität zu Lübeck, Institut für Medizinische Informatik |
| P16: Learning the Update Operator for 2D/3D Image Registration Srikrishna Jaganathan et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P30: Multi-modal Unsupervised Domain Adaptation for Deformable Registration Based on Maximum Classifier Discrepancy Christian N. Kruse et al. Universität zu Lübeck, Institut für Medizinische Informatik |
| P45: Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering Karthik Shetty et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
Datasets
| P17: Abstract: Generation of Annotated Brain Tumor MRIs with Tumor-induced Tissue Deformations for Training and Assessment of Neural Networks Hristina Uzunova et al. Universität zu Lübeck, Institut für Medizinische Informatik |
| P31: Abstract: A Completely Annotated Whole Slide Image Dataset of Canine Breast Cancer to Aid Human Breast Cancer Research Marc Aubreville et al. Technische Hochschule Ingolstadt, Computer Science |
| P46: Abstract: Are Fast Labeling Methods Reliable?: A Case Study of Computer-aided Expert Annotations on Microscopy Slides Christian Marzahl et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
Generative Adversarial Networks
| P32: Acquisition Parameter-conditioned Magnetic Resonance Image-to-image Translation Jonas Denck et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P33: Fine-tuning Generative Adversarial Networks Using Metaheuristics: A Case Study on Barrett's Esophagus Identification Luis A. Souza et al. Federal University of São Carlos - UFScar, Department of Computing |
Time Series and Video Analysis
| P47: Abstract: Time Matters: Handling Spatio-temporal Perfusion Information for Automated Treatment in Cerebral Ischemia Scoring Maximilian Nielsen et al. Universität Hamburg, Institut für Computational Neuroscience |
| S01: Abstract: Multi-camera, Multi-person, and Real-time Fall Detection using Long Short Term Memory Christian Heinrich et al. Technische Universität Braunschweig, Peter L. Reichertz Institute for Medical Informatics |
Visualization
| P48: A Geometric and Textural Model of the Colon as Ground Truth for Deep Learning-based 3D-reconstruction Ralf Hackner et al. Fraunhofer IIS, Erlangen |
| P49: Deep Learning-basierte Oberflächenrekonstruktion aus Binärmasken Carina Tschigor et al. Fraunhofer MEVIS, Bremen |
| S04: Interactive Visualization of 3D CNN Relevance Maps to Aid Model Comprehensibility: Application to the Detection of Alzheimer’s Disease in MRI Images Martin Dyrba et al. Deutsches Zentrum für Neurodegenerative Erkrankungen, Rostock |
| S05: Abstract: VirtualDSA++: Automated Segmentation, Vessel Labeling, Occlusion Detection, and Graph Search on CT Angiography Data Florian Thamm et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
Denoising
| P50: A Novel Trilateral Filter for Digital Subtraction Angiography Purvi Tripathi et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
| P51: Abstract: JBFnet: Low Dose CT-denoising by Trainable Joint Bilateral Filtering Patwari, Mayank et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Mustererkennung |
Neural Networks in General
| P34: Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data Zijin Yang et al. Friedrich-Alexander Universität Erlangen-Nürnberg, Lehrstuhl für Machine Intelligence |