446 citations · 2.3k across the 135 of their papers we have counts for
52 papers · 1 filter
Self-Attention Equipped Graph Convolutions for Disease Prediction
Anees Kazi, S. Arvind krishna, Shayan Shekarforoush +3
Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test, demographics, etc.) data can be collected together and used for disease prediction. Such…
Adversarial Signal Denoising with Encoder-Decoder Networks
Leslie Casas, Attila Klimmek, Nassir Navab +1
The presence of noise is common in signal processing regardless the signal type. Deep neural networks have shown good performance in noise removal, especially on the image domain.…
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data
Fabian Manhardt, Diego Martin Arroyo, Christian Rupprecht +4
3D object detection and pose estimation from a single image are two inherently ambiguous problems. Oftentimes, objects appear similar from different viewpoints due to shape symmetr…
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control
Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab +1
We introduce Bayesian QuickNAT for the automated quality control of whole-brain segmentation on MRI T1 scans. Next to the Bayesian fully convolutional neural network, we also prese…
Redefining Ultrasound Compounding: Computational Sonography
Rüdiger Göbl, Diana Mateus, Christoph Hennersperger +2
Freehand three-dimensional ultrasound (3D-US) has gained considerable interest in research, but even today suffers from its high inter-operator variability in clinical practice. Th…
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions
Ghazal Ghazaei, Iro Laina, Christian Rupprecht +3
Humans excel in grasping and manipulating objects because of their life-long experience and knowledge about the 3D shape and weight distribution of objects. However, the lack of su…
Adversarial Semantic Scene Completion from a Single Depth Image
Yida Wang, David Joseph Tan, Nassir Navab +1
We propose a method to reconstruct, complete and semantically label a 3D scene from a single input depth image. We improve the accuracy of the regressed semantic 3D maps by a novel…
The speaker-independent lipreading play-off; a survey of lipreading machines
Jake Burton, David Frank, Madhi Saleh +2
Lipreading is a difficult gesture classification task. One problem in computer lipreading is speaker-independence. Speaker-independence means to achieve the same accuracy on test s…
Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach
Amal Lahiani, Jacob Gildenblat, Irina Klaman +3
Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has be…
InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation
Shubham Kumar, Sailesh Conjeti, Abhijit Guha Roy +2
We present a novel, parameter-efficient and practical fully convolutional neural network architecture, termed InfiNet, aimed at voxel-wise semantic segmentation of infant brain MRI…
Deep Model-Based 6D Pose Refinement in RGB
Fabian Manhardt, Wadim Kehl, Nassir Navab +1
We present a novel approach for model-based 6D pose refinement in color data. Building on the established idea of contour-based pose tracking, we teach a deep neural network to pre…
Incremental Adversarial Learning for Optimal Path Planning
Salvatore Virga, Christian Rupprecht, Nassir Navab +1
Path planning plays an essential role in many areas of robotics. Various planning techniques have been presented, either focusing on learning a specific task from demonstrations or…
Generating Highly Realistic Images of Skin Lesions with GANs
Christoph Baur, Shadi Albarqouni, Nassir Navab
As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses prob…
GANs for Medical Image Analysis
Salome Kazeminia, Christoph Baur, Arjan Kuijper +4
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical…
Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks
Abhijit Guha Roy, Nassir Navab, Christian Wachinger
In a wide range of semantic segmentation tasks, fully convolutional neural networks (F-CNNs) have been successfully leveraged to achieve state-of-the-art performance. Architectural…
Deep Learned Full-3D Object Completion from Single View
Dario Rethage, Federico Tombari, Felix Achilles +1
3D geometry is a very informative cue when interacting with and navigating an environment. This writing proposes a new approach to 3D reconstruction and scene understanding, which…
Fully-Convolutional Point Networks for Large-Scale Point Clouds
Dario Rethage, Johanna Wald, Jürgen Sturm +2
This work proposes a general-purpose, fully-convolutional network architecture for efficiently processing large-scale 3D data. One striking characteristic of our approach is its ab…
Towards Robotic Eye Surgery: Marker-free, Online Hand-eye Calibration using Optical Coherence Tomography Images
Mingchuan Zhou, Mahdi Hamad, Jakob Weiss +7
Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation. Image guided robot-assisted surgery (RAS) is a promising sol…
Human Motion Analysis with Deep Metric Learning
Huseyin Coskun, David Joseph Tan, Sailesh Conjeti +2
Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identi- fication and action retrieval. N…
Peeking Behind Objects: Layered Depth Prediction from a Single Image
Helisa Dhamo, Keisuke Tateno, Iro Laina +2
While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects. This limi…
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
Santiago Estrada, Sailesh Conjeti, Muneer Ahmad +2
Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance…
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis
Deepa Gunashekar, Sailesh Conjeti, Abhijit Guha Roy +2
Cross modal image syntheses is gaining significant interests for its ability to estimate target images of a different modality from a given set of source images,like estimating MR…
Augmented Reality-based Feedback for Technician-in-the-loop C-arm Repositioning
Mathias Unberath, Javad Fotouhi, Jonas Hajek +5
Interventional C-arm imaging is crucial to percutaneous orthopedic procedures as it enables the surgeon to monitor the progress of surgery on the anatomy level. Minimally invasive…
Initialize globally before acting locally: Enabling Landmark-free 3D US to MRI Registration
Julia Rackerseder, Maximilian Baust, Rüdiger Göbl +2
Registration of partial-view 3D US volumes with MRI data is influenced by initialization. The standard of practice is using extrinsic or intrinsic landmarks, which can be very tedi…