578 citations · 2.5k across the 216 of their papers we have counts for
20 papers · 1 filter
A Comparative Analysis of Virtual Reality Head-Mounted Display Systems
Arian Mehrfard, Javad Fotouhi, Giacomo Taylor +3
With recent advances of Virtual Reality (VR) technology, the deployment of such will dramatically increase in non-entertainment environments, such as professional education and tra…
Radar Emitter Classification with Attribute-specific Recurrent Neural Networks
Paolo Notaro, Magdalini Paschali, Carsten Hopke +2
Radar pulse streams exhibit increasingly complex temporal patterns and can no longer rely on a purely value-based analysis of the pulse attributes for the purpose of emitter classi…
Signal Clustering with Class-independent Segmentation
Stefano Gasperini, Magdalini Paschali, Carsten Hopke +2
Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches. In this paper we propose a Deep Learning-based clus…
Improving Feature Attribution through Input-specific Network Pruning
Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja +3
Attributing the output of a neural network to the contribution of given input elements is a way of shedding light on the black-box nature of neural networks. Due to the complexity…
CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions
Tom Vercauteren, Mathias Unberath, Nicolas Padoy +1
Data-driven computational approaches have evolved to enable extraction of information from medical images with a reliability, accuracy and speed which is already transforming their…
Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories
Jan-Nico Zaech, Cong Gao, Bastian Bier +4
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts ar…
Learn to Estimate Labels Uncertainty for Quality Assurance
Agnieszka Tomczack, Nassir Navab, Shadi Albarqouni
Deep Learning sets the state-of-the-art in many challenging tasks showing outstanding performance in a broad range of applications. Despite its success, it still lacks robustness h…
Learn to Segment Organs with a Few Bounding Boxes
Abhijeet Parida, Arianne Tran, Nassir Navab +1
Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology. Deep neural networks…
ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image
Yida Wang, David Joseph Tan, Nassir Navab +1
We propose a novel model for 3D semantic completion from a single depth image, based on a single encoder and three separate generators used to reconstruct different geometric and s…
Object-Driven Multi-Layer Scene Decomposition From a Single Image
Helisa Dhamo, Nassir Navab, Federico Tombari
We present a method that tackles the challenge of predicting color and depth behind the visible content of an image. Our approach aims at building up a Layered Depth Image (LDI) fr…
Towards Unsupervised Image Captioning with Shared Multimodal Embeddings
Iro Laina, Christian Rupprecht, Nassir Navab
Understanding images without explicit supervision has become an important problem in computer vision. In this paper, we address image captioning by generating language descriptions…
RIO: 3D Object Instance Re-Localization in Changing Indoor Environments
Johanna Wald, Armen Avetisyan, Nassir Navab +2
In this work, we introduce the task of 3D object instance re-localization (RIO): given one or multiple objects in an RGB-D scan, we want to estimate their corresponding 6DoF poses…
Grasp Type Estimation for Myoelectric Prostheses using Point Cloud Feature Learning
Ghazal Ghazaei, Federico Tombari, Nassir Navab +1
Prosthetic hands can help people with limb difference to return to their life routines. Commercial prostheses, however have several limitations in providing an acceptable dexterity…
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
Janis Postels, Francesco Ferroni, Huseyin Coskun +2
We present a sampling-free approach for computing the epistemic uncertainty of a neural network. Epistemic uncertainty is an important quantity for the deployment of deep neural ne…
Reflective-AR Display: An Interaction Methodology for Virtual-Real Alignment in Medical Robotics
Javad Fotouhi, Tianyu Song, Arian Mehrfard +8
Robot-assisted minimally invasive surgery has shown to improve patient outcomes, as well as reduce complications and recovery time for several clinical applications. While increasi…
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition
Huseyin Coskun, Zeeshan Zia, Bugra Tekin +4
The lack of large-scale real datasets with annotations makes transfer learning a necessity for video activity understanding. We aim to develop an effective method for few-shot tran…
Uncertainty-based graph convolutional networks for organ segmentation refinement
Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni
Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks ha…
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Anees Kazi, Shayan shekarforoush, S. Arvind krishna +6
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks (GC…
Adversarial Networks for Camera Pose Regression and Refinement
Mai Bui, Christoph Baur, Nassir Navab +2
Despite recent advances on the topic of direct camera pose regression using neural networks, accurately estimating the camera pose of a single RGB image still remains a challenging…
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni +5
We demonstrate the feasibility of a fully automatic computer-aided diagnosis (CAD) tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray ima…