Nassir Navab

Nassir Navab is Full Professor and Director of the Chair for Computer Aided Medical Procedures and Augmented Reality (CAMP) at TUM, where he has been a full professor since 2003, and an adjunct professor at Johns Hopkins University. He is an IEEE Fellow and ELLIS Fellow; honours include the MICCAI Enduring Impact Award (2021). He was the PhD advisor of Shadi Albarqouni.

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Funded projects 4 filter all →

Development of a Deep Learning Toolkit for MRI-Guided Online Adaptive Radiotherapy

Digital staining with confidence estimation for hematological imaging

Modelling Uncertainty in Deep Learning for Medical Applications

Uncertainty Aware Methods for Camera Pose Estimation and Relocalization

Publications 81 filter all →

Enhancing patient-specific deep learning based segmentation for abdominal magnetic resonance imaging-guided radiation therapy: a framework conditioned on prior segmentation

CloverNet--Leveraging Planning Annotations for Enhanced Procedural MR Segmentation: An Application to Adaptive Radiation Therapy

Digital staining of white blood cells with confidence estimation

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?

Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study

Butterfly-Net: Spatial-Temporal Architecture For Medical Image Segmentation

Eine computergestützte automatische Polypencharakterisierung von Hyperplasten, Adenomen und Serratierten Adenomen im Kolorektum-Ergebnisse der CASSANDRA Studie

FedPerl: Semi-Supervised Peer Learning for Skin Lesion Classification

Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias

Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI

Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks

Multi-task multi-domain learning for digital staining and classification of leukocytes

An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation

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