Latest

• BA/MA Thesis: Deep Learning for Inherited Retinal Diseases Detection
• CIR/06/2023: Ph.D. position in AI in Medicine - Robustness and Explainability (m/f/d)
• MA Thesis: Deep Learning based detection model of the temporal and axillary artery in suspected giant cell arteritis in ultrasound images
• Federated disentangled representation learning for unsupervised brain anomaly detection
• MA Thesis: Deep Learning based model for detection and grading of prostate cancer using mpMRI and MR-Fingerprinting
• MA Thesis: Deep Learning-based method for virtual ECV in cardiac magnetic resonance imaging
• FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
• Course: Introduction to Machine Learning
• Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
• Federated disentangled representation learning for unsupervised brain anomaly detection
• Anomaly-aware multiple instance learning for rare anemia disorder classification
• FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
• ROAM: Random layer mixup for semi-supervised learning in medical images
• Semi-Supervised Federated Peer Learning for Skin Lesion Classification
• TriMix: Virtual embeddings and self-consistency for self-supervised learning
• Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
• What can we learn about a generated image corrupting its latent representation?
• Seminar: Federated Learning in Healthcare (SoSe2021)
• Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study
• Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study
• Butterfly-Net: Spatial-Temporal Architecture For Medical Image Segmentation
• FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
• Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection
• FedPerl: Semi-Supervised Peer Learning for Skin Lesion Classification
• Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias
• Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations
• Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI
• Semi-Supervised Few-Shot Learning with Prototypical Random Walks
• Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks
• The Federated Tumor Segmentation (FeTS) Challenge
• The OOD Blind Spot of Unsupervised Anomaly Detection
• Multi-task multi-domain learning for digital staining and classification of leukocytes
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
• Ascertaining the Pose of an X-Ray Unit Relative to an Object on the Basis of a Digital Model of the Object
• Determining a Pose of an Object in the Surroundings of the Object by Means of Multi-Task Learning
• Seminar: Federated Learning in Healthcare (WiSe2020)
• The Future of Digital Health with Federated Learning
• 6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
• Fairness by Learning Orthogonal Disentangled Representations
• Uncertainty-based graph convolutional networks for organ segmentation refinement
• Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency
• A learning without forgetting approach to incorporate artifact knowledge in polyp localization tasks
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
• Attention Based Multiple Instance Learning for Classification of Blood Cell Disorders
• Bayesian Skip-Autoencoders for Unsupervised Hyperintense Anomaly Detection in High Resolution Brain Mri
• Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning
• GANs for medical image analysis
• Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography
• Inverse Distance Aggregation for Federated Learning with Non-IID Data
• On the Fairness of Privacy-Preserving Representations in Medical Applications
• Polyp-artifact relationship analysis using graph inductive learned representations
• Precise proximal femur fracture classification for interactive training and surgical planning.
• Retinal Layer Segmentation Reformulated as OCT Language Processing
• ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
• Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
• SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI
• Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting
• Method for determining a pose of an object in an environment of the object using multi task learning and control device
• Preliminary Meeting for the seminar on Federated Learning in Healthcare
• Preliminary Meeting for the seminar on Federated Learning in Healthcare
• Adaptive image-feature learning for disease classification using inductive graph networks
• Adversarial Networks for Camera Pose Regression and Refinement
• Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data: First MICCAI Workshop, DART 2019, and First International Workshop, MIL3ID 2019, Shenzhen, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings
• Focal loss for artefact detection in medical endoscopy
• Fusing unsupervised and supervised deep learning for white matter lesion segmentation
• Graph Convolution Based Attention Model for Personalized Disease Prediction
• InceptionGCN: receptive field aware graph convolutional network for disease prediction
• Investigation of Focal Loss in Deep Learning Models For Femur Fractures Classification
• Learn to estimate labels uncertainty for quality assurance
• Learn to Segment Organs with a Few Bounding Boxes
• Learning interpretable disentangled representations using adversarial vaes
• Learning Interpretable Features via Adversarially Robust Optimization
• Learning-based x-ray image denoising utilizing model-based image simulations
• MRI to CT Translation with GANs
• Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss
• Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
• Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system
• Self-attention equipped graph convolutions for disease prediction
• Semi-Supervised Few-Shot Learning with Local and Global Consistency
• Semi-Supervised Few-Shot Learning with Prototypical Random Walks
• Staingan: Stain style transfer for digital histological images
• Towards an Interactive and Interpretable CAD System to Support Proximal Femur Fracture Classification
• Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach
• Determination of the pose of an x-ray unit relative to an object using a digital model of the object
• 7th Joint International Workshop, CVII-STENT (Intravascular Imaging and Computer Assisted Stenting) 2018 and Third International Workshop, LABELS (Large-Scale Annotation of Biomedical Data and Expert Label Synthesis) 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings
• A photon recycling approach to the denoising of ultra-low dose X-ray sequences
• Capsule networks against medical imaging data challenges
• Deep autoencoding models for unsupervised anomaly segmentation in brain MR images
• Deep learning with synthetic data for free water elimination in diffusion MRI
• Domain and geometry agnostic CNNs for left atrium segmentation in 3D ultrasound
• Fine-Tuning Deep Learning by Crowd Participation
• GANs for medical image analysis
• Generating highly realistic images of skin lesions with GANs
• Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair
• Intravascular Imaging and Computer Assisted Stenting and Large-scale Annotation of Biomedical Data and Expert Label Synthesis: 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings
• Multiple device segmentation for fluoroscopic imaging using multi-task learning
• Scene coordinate and correspondence learning for image-based localization
• Weakly-supervised localization and classification of proximal femur fractures
• When regression meets manifold learning for object recognition and pose estimation
• X-ray Depthmaps: Revealing the Hidden Structures
• Automatic classification of proximal femur fractures based on attention models
• Machine Learning for Biomedical Applications: From Crowdsourcing to Deep Learning
• Semi-supervised deep learning for fully convolutional networks
• X-ray in-depth decomposition: Revealing the latent structures
• X-Ray PoseNet: 6 DoF pose estimation for mobile X-Ray devices
• Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images
• Cathnets: detection and single-view depth prediction of catheter electrodes
• Playsourcing: a novel concept for knowledge creation in biomedical research
• Single-view X-ray depth recovery: toward a novel concept for image-guided interventions
• Structure-preserving color normalization and sparse stain separation for histological images
• Gradient projection for regularized cryo-electron tomographic reconstruction
• Structure-preserved color normalization for histological images
• Developing empirical models for estimating global solar radiation in Gaza Strip, Palestine
• Study on the Optimum Tilt Angle and Orientation for Photovoltaic Panels and Feasibility Study of One axis-two positions tracking Solar PV in Palestine
• Steps towards Establishing Telemedicine Center in Palestine