578 citations · 2.5k across the 216 of their papers we have counts for
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Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation
Haowen Deng, Mai Bui, Nassir Navab +3
In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can naturally handle pose-related uncertainties and ambiguities arising in almost all real life app…
An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
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…
Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods
Ashkan Khakzar, Soroosh Baselizadeh, Nassir Navab
Saliency methods interpret the prediction of a neural network by showing the importance of input elements for that prediction. A popular family of saliency methods utilize gradient…
Self-Supervised Out-of-Distribution Detection in Brain CT Scans
Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy +2
Medical imaging data suffers from the limited availability of annotation because annotating 3D medical data is a time-consuming and expensive task. Moreover, even if the annotation…
Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration
Matthias Grimm, Javier Esteban, Mathias Unberath +1
Fully automatic X-ray to CT registration requires a solid initialization to provide an initial alignment within the capture range of existing intensity-based registrations. This wo…
Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback
Zhongliang Jiang, Zhenyu Li, Matthias Grimm +6
Ultrasound (US) imaging is widely employed for diagnosis and staging of peripheral vascular diseases (PVD), mainly due to its high availability and the fact it does not emit radiat…
Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration
Mahdi Saleh, Shervin Dehghani, Benjamin Busam +2
3D Point clouds are a rich source of information that enjoy growing popularity in the vision community. However, due to the sparsity of their representation, learning models based…
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds
Stefano Gasperini, Mohammad-Ali Nikouei Mahani, Alvaro Marcos-Ramiro +2
Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and e…
SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion
Shun-Cheng Wu, Keisuke Tateno, Nassir Navab +1
Real-time scene reconstruction from depth data inevitably suffers from occlusion, thus leading to incomplete 3D models. Partial reconstructions, in turn, limit the performance of a…
RGB-D SLAM with Structural Regularities
Yanyan Li, Raza Yunus, Nikolas Brasch +2
This work proposes a RGB-D SLAM system specifically designed for structured environments and aimed at improved tracking and mapping accuracy by relying on geometric features that a…
Polyp-artifact relationship analysis using graph inductive learned representations
Roger D. Soberanis-Mukul, Shadi Albarqouni, Nassir Navab
The diagnosis process of colorectal cancer mainly focuses on the localization and characterization of abnormal growths in the colon tissue known as polyps. Despite recent advances…
I Like to Move It: 6D Pose Estimation as an Action Decision Process
Benjamin Busam, Hyun Jun Jung, Nassir Navab
Object pose estimation is an integral part of robot vision and AR. Previous 6D pose retrieval pipelines treat the problem either as a regression task or discretize the pose space t…
Inverse Distance Aggregation for Federated Learning with Non-IID Data
Yousef Yeganeh, Azade Farshad, Nassir Navab +1
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more…
SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification
Yida Wang, David Joseph Tan, Nassir Navab +1
Point clouds are often the default choice for many applications as they exhibit more flexibility and efficiency than volumetric data. Nevertheless, their unorganized nature -- poin…
A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance
Mareike Thies, Jan-Nico Zäch, Cong Gao +4
During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomograp…
Continual Class Incremental Learning for CT Thoracic Segmentation
Abdelrahman Elskhawy, Aneta Lisowska, Matthias Keicher +3
Deep learning organ segmentation approaches require large amounts of annotated training data, which is limited in supply due to reasons of confidentiality and the time required for…
Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments
Yanyan Li, Nikolas Brasch, Yida Wang +2
In this paper a low-drift monocular SLAM method is proposed targeting indoor scenarios, where monocular SLAM often fails due to the lack of textured surfaces. Our approach decouple…
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching
Patrick Ruhkamp, Ruiqi Gong, Nassir Navab +1
Feature based visual odometry and SLAM methods require accurate and fast correspondence matching between consecutive image frames for precise camera pose estimation in real-time. C…
Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
Ario Sadafi, Asya Makhro, Anna Bogdanova +4
Red blood cells are highly deformable and present in various shapes. In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis.…
Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks
Daniil Pakhomov, Wei Shen, Nassir Navab
Surgical tool segmentation in endoscopic images is an important problem: it is a crucial step towards full instrument pose estimation and it is used for integration of pre- and int…
Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery
Daniil Pakhomov, Nassir Navab
Segmentation of surgical instruments is an important problem in robot-assisted surgery: it is a crucial step towards full instrument pose estimation and is directly used for maskin…
Augment Yourself: Mixed Reality Self-Augmentation Using Optical See-through Head-mounted Displays and Physical Mirrors
Mathias Unberath, Kevin Yu, Roghayeh Barmaki +2
Optical see-though head-mounted displays (OST HMDs) are one of the key technologies for merging virtual objects and physical scenes to provide an immersive mixed reality (MR) envir…
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty
Amelia Jiménez-Sánchez, Diana Mateus, Sonja Kirchhoff +5
An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We rely on the commonly used AO sys…
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
Christoph Baur, Benedikt Wiestler, Shadi Albarqouni +1
Brain pathologies can vary greatly in size and shape, ranging from few pixels (i.e. MS lesions) to large, space-occupying tumors. Recently proposed Autoencoder-based methods for un…