Search Results for author: Nassir Navab

Found 228 papers, 56 papers with code

Semantic Image Alignment for Vehicle Localization

no code implementations8 Oct 2021 Markus Herb, Matthias Lemberger, Marcel M. Schmitt, Alexander Kurz, Tobias Weiherer, Nassir Navab, Federico Tombari

Accurate and reliable localization is a fundamental requirement for autonomous vehicles to use map information in higher-level tasks such as navigation or planning.

Autonomous Vehicles Semantic Segmentation +1

Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation

no code implementations24 Sep 2021 Mert Asim Karaoglu, Nikolas Brasch, Marijn Stollenga, Wolfgang Wein, Nassir Navab, Federico Tombari, Alexander Ladikos

The results of our experiments show that the proposed method improves the network's performance on real images by a considerable margin and can be employed in 3D reconstruction pipelines.

3D Reconstruction Depth Estimation

MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation

no code implementations18 Sep 2021 Anastasia Makarevich, Azade Farshad, Vasileios Belagiannis, Nassir Navab

In this work, we present MetaMedSeg, a gradient-based meta-learning algorithm that redefines the meta-learning task for the volumetric medical data with the goal to capture the variety between the slices.

Medical Image Segmentation Meta-Learning

Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs

no code implementations ICCV 2021 Helisa Dhamo, Fabian Manhardt, Nassir Navab, Federico Tombari

Scene graphs are representations of a scene, composed of objects (nodes) and inter-object relationships (edges), proven to be particularly suited for this task, as they allow for semantic control on the generated content.

SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation

2 code implementations ICCV 2021 Yan Di, Fabian Manhardt, Gu Wang, Xiangyang Ji, Nassir Navab, Federico Tombari

Directly regressing all 6 degrees-of-freedom (6DoF) for the object pose (e. g. the 3D rotation and translation) in a cluttered environment from a single RGB image is a challenging problem.

6D Pose Estimation 6D Pose Estimation using RGB +1

Unconditional Scene Graph Generation

no code implementations ICCV 2021 Sarthak Garg, Helisa Dhamo, Azade Farshad, Sabrina Musatian, Nassir Navab, Federico Tombari

Scene graphs, composed of nodes as objects and directed-edges as relationships among objects, offer an alternative representation of a scene that is more semantically grounded than images.

Anomaly Detection Graph Generation +2

Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry

no code implementations10 Aug 2021 Markus Krönke, Christine Eilers, Desislava Dimova, Melanie Köhler, Gabriel Buschner, Lilit Mirzojan, Lemonia Konstantinidou, Marcus R. Makowski, James Nagarajah, Nassir Navab, Wolfgang Weber, Thomas Wendler

Conclusion: Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times.

R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes

no code implementations10 Aug 2021 Stefano Gasperini, Patrick Koch, Vinzenz Dallabetta, Nassir Navab, Benjamin Busam, Federico Tombari

While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue.

Autonomous Vehicles Monocular Depth Estimation

U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction

no code implementations29 Jul 2021 Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni, Magdalini Paschali, Tobias Czempiel, Egon Burian, Marcus R. Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler

Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.

Decision Making Graph Attention

Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP

1 code implementation26 Jul 2021 Daniil Pakhomov, Sanchit Hira, Narayani Wagle, Kemar E. Green, Nassir Navab

Derived regions are consistent across different images and coincide with human-defined semantic classes on some datasets.

Semantic Segmentation

Multimodal Semantic Scene Graphs for Holistic Modeling of Surgical Procedures

no code implementations9 Jun 2021 Ege Özsoy, Evin Pınar Örnek, Ulrich Eck, Federico Tombari, Nassir Navab

We then use MSSG to introduce a dynamically generated graphical user interface tool for surgical procedure analysis which could be used for many applications including process optimization, OR design and automatic report generation.

Deep Direct Volume Rendering: Learning Visual Feature Mappings From Exemplary Images

no code implementations9 Jun 2021 Jakob Weiss, Nassir Navab

In this work, we introduce Deep Direct Volume Rendering (DeepDVR), a generalization of DVR that allows for the integration of deep neural networks into the DVR algorithm.

Colorization Neural Rendering

GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference

no code implementations8 Apr 2021 Mahsa Ghorbani, Mojtaba Bahrami, Anees Kazi, Mahdieh SoleymaniBaghshah, Hamid R. Rabiee, Nassir Navab

The soft pseudo-labels are then used to train a deep student network for disease prediction of unseen test data for which the graph modality is unavailable.

Disease Prediction graph construction +1

Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models

1 code implementation4 Apr 2021 Ashkan Khakzar, Sabrina Musatian, Jonas Buchberger, Icxel Valeriano Quiroz, Nikolaus Pinger, Soroosh Baselizadeh, Seong Tae Kim, Nassir Navab

We present our findings using publicly available chest pathologies (CheXpert, NIH ChestX-ray8) and COVID-19 datasets (BrixIA, and COVID-19 chest X-ray segmentation dataset).

COVID-19 Diagnosis

IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction

no code implementations29 Mar 2021 Anees Kazi, Soroush Farghadani, Nassir Navab

The main novelty lies in the interpretable attention module (IAM), which directly operates on multi-modal features.

Decision Making Disease Prediction +2

GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images

no code implementations19 Mar 2021 Aadhithya Sankar, Matthias Keicher, Rami Eisawy, Abhijeet Parida, Franz Pfister, Seong Tae Kim, Nassir Navab

Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically generated images that can be useful in training other models that require a large number of labelled or unlabelled data.

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

1 code implementation12 Mar 2021 Seong Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler

Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.

Computed Tomography (CT) COVID-19 Image Segmentation +1

Peer Learning for Skin Lesion Classification

1 code implementation5 Mar 2021 Tariq Bdair, Nassir Navab, Shadi Albarqouni

Federated learning has been recently introduced to train machine learning models in a privacy-preserved distributed fashion demanding annotated data at the clients, which is usually expensive and not available, especially in the medical field.

Classification Federated Learning +4

OperA: Attention-Regularized Transformers for Surgical Phase Recognition

no code implementations5 Mar 2021 Tobias Czempiel, Magdalini Paschali, Daniel Ostler, Seong Tae Kim, Benjamin Busam, Nassir Navab

In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences.

RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data

no code implementations27 Feb 2021 Mahsa Ghorbani, Anees Kazi, Mahdieh Soleymani Baghshah, Hamid R. Rabiee, Nassir Navab

Due to the nature of such medical datasets, the class imbalance is a familiar issue in the field of disease prediction.

Disease Prediction

Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation

no code implementations20 Dec 2020 Haowen Deng, Mai Bui, Nassir Navab, Leonidas Guibas, Slobodan Ilic, Tolga Birdal

For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify.

Camera Relocalization Pose Estimation

An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation

1 code implementation6 Dec 2020 Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni

In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.

Graph Learning

Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods

no code implementations1 Dec 2020 Ashkan Khakzar, Soroosh Baselizadeh, Nassir Navab

In this work, we empirically show that two approaches for handling the gradient information, namely positive aggregation, and positive propagation, break these methods.

Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration

no code implementations14 Nov 2020 Matthias Grimm, Javier Esteban, Mathias Unberath, Nassir Navab

First, a neural network is trained once to detect a set of anatomical landmarks on simulated X-rays.

Self-Supervised Out-of-Distribution Detection in Brain CT Scans

no code implementations10 Nov 2020 Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy, Franz Pfister, Nassir Navab

To address these issues, recently, unsupervised deep anomaly detection methods that train the model on large-sized normal scans and detect abnormal scans by calculating reconstruction error have been reported.

Anomaly Detection Out-of-Distribution Detection +1

Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds

no code implementations28 Oct 2020 Stefano Gasperini, Mohammad-Ali Nikouei Mahani, Alvaro Marcos-Ramiro, Nassir Navab, Federico Tombari

Panoptic segmentation has recently unified semantic and instance segmentation, previously addressed separately, thus taking a step further towards creating more comprehensive and efficient perception systems.

Instance Segmentation Panoptic Segmentation

SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion

2 code implementations26 Oct 2020 Shun-Cheng Wu, Keisuke Tateno, Nassir Navab, Federico Tombari

We propose a framework that ameliorates this issue by performing scene reconstruction and semantic scene completion jointly in an incremental and real-time manner, based on an input sequence of depth maps.

RGB-D SLAM with Structural Regularities

1 code implementation15 Oct 2020 Yanyan Li, Raza Yunus, Nikolas Brasch, Nassir Navab, Federico Tombari

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 are extracted from the surrounding.


I Like to Move It: 6D Pose Estimation as an Action Decision Process

no code implementations26 Sep 2020 Benjamin Busam, Hyun Jun Jung, Nassir Navab

We change this paradigm and reformulate the problem as an action decision process where an initial pose is updated in incremental discrete steps that sequentially move a virtual 3D rendering towards the correct solution.

6D Pose Estimation Pose Prediction +1

Polyp-artifact relationship analysis using graph inductive learned representations

no code implementations15 Sep 2020 Roger D. Soberanis-Mukul, Shadi Albarqouni, Nassir Navab

In inference, we use this classifier to analyze a second graph, generated from artifact and polyp predictions given by region proposal networks.

Graph Representation Learning Object Localization +1

Inverse Distance Aggregation for Federated Learning with Non-IID Data

no code implementations17 Aug 2020 Yousef Yeganeh, Azade Farshad, Nassir Navab, Shadi Albarqouni

Federated learning (FL) has been a promising approach in the field of medical imaging in recent years.

Federated Learning

A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance

no code implementations14 Aug 2020 Mareike Thies, Jan-Nico Zäch, Cong Gao, Russell Taylor, Nassir Navab, Andreas Maier, Mathias Unberath

We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i. e. verification of screw placement.

Tomographic Reconstructions

Continual Class Incremental Learning for CT Thoracic Segmentation

no code implementations12 Aug 2020 Abdelrahman Elskhawy, Aneta Lisowska, Matthias Keicher, Josep Henry, Paul Thomson, Nassir Navab

In this work, we evaluate FT and LwF for class incremental learning in multi-organ segmentation using the publicly available AAPM dataset.

class-incremental learning Incremental Learning

Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments

1 code implementation5 Aug 2020 Yanyan Li, Nikolas Brasch, Yida Wang, Nassir Navab, Federico Tombari

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.


DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching

no code implementations31 Jul 2020 Patrick Ruhkamp, Ruiqi Gong, Nassir Navab, Benjamin Busam

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.

Pose Estimation Visual Odometry

Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks

no code implementations9 Jul 2020 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 intra-operative images into the endoscopic view.

Image-to-Image Translation Pose Estimation +1

Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery

no code implementations8 Jul 2020 Daniil Pakhomov, Nassir Navab

To account for reduced accuracy of the discovered light-weight deep residual network and avoid adding any additional computational burden, we perform a differentiable search over dilation rates for residual units of our network.

Pose Estimation

Augment Yourself: Mixed Reality Self-Augmentation Using Optical See-through Head-mounted Displays and Physical Mirrors

no code implementations6 Jul 2020 Mathias Unberath, Kevin Yu, Roghayeh Barmaki, Alex Johnson, Nassir Navab

Consequently, most MR applications that are centered around the user, such as virtual dressing rooms or learning of body movements, cannot be realized with HMDs.

Mixed Reality

Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI

no code implementations23 Jun 2020 Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab

Brain pathologies can vary greatly in size and shape, ranging from few pixels (i. e. MS lesions) to large, space-occupying tumors.

Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation

no code implementations21 May 2020 Hong Joo Lee, Seong Tae Kim, Hakmin Lee, Nassir Navab, Yong Man Ro

Experimental results show that the proposed method could provide useful uncertainty information by Bayesian approximation with the efficient ensemble model generation and improve the predictive performance.

Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)

1 code implementation14 May 2020 Gerome Vivar, Anees Kazi, Hendrik Burwinkel, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi

As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multigraph Geometric Matrix Completion (MGMC).

Classification Disease Prediction +3

Decision Support for Intoxication Prediction Using Graph Convolutional Networks

no code implementations2 May 2020 Hendrik Burwinkel, Matthias Keicher, David Bani-Harouni, Tobias Zellner, Florian Eyer, Nassir Navab, Seyed-Ahmad Ahmadi

Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame.

Explicit Domain Adaptation with Loosely Coupled Samples

no code implementations24 Apr 2020 Oliver Scheel, Loren Schwarz, Nassir Navab, Federico Tombari

In this work we propose a transfer learning framework, core of which is learning an explicit mapping between domains.

Autonomous Driving Domain Adaptation +3

Colon Shape Estimation Method for Colonoscope Tracking using Recurrent Neural Networks

no code implementations20 Apr 2020 Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku MORI

We propose a colon deformation estimation method using RNN and obtain the colonoscope shape from electromagnetic sensors during its insertion into the colon.

Colonoscope tracking method based on shape estimation network

no code implementations20 Apr 2020 Masahiro Oda, Holger R. Roth, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Nassir Navab, Kensaku MORI

We utilize the shape estimation network (SEN), which estimates deformed colon shape during colonoscope insertions.

6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference

1 code implementation ECCV 2020 Mai Bui, Tolga Birdal, Haowen Deng, Shadi Albarqouni, Leonidas Guibas, Slobodan Ilic, Nassir Navab

We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.

Camera Relocalization

Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions

no code implementations CVPR 2020 Johanna Wald, Helisa Dhamo, Nassir Navab, Federico Tombari

In our work we focus on scene graphs, a data structure that organizes the entities of a scene in a graph, where objects are nodes and their relationships modeled as edges.

3D Semantic Segmentation Scene Understanding

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

1 code implementation7 Apr 2020 Christoph Baur, Stefan Denner, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab

Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI.

Experimental Design Unsupervised Anomaly Detection +1

Semantic Image Manipulation Using Scene Graphs

1 code implementation CVPR 2020 Helisa Dhamo, Azade Farshad, Iro Laina, Nassir Navab, Gregory D. Hager, Federico Tombari, Christian Rupprecht

In our work, we address the novel problem of image manipulation from scene graphs, in which a user can edit images by merely applying changes in the nodes or edges of a semantic graph that is generated from the image.

Image Inpainting Image Manipulation +1

Confident Coreset for Active Learning in Medical Image Analysis

no code implementations5 Apr 2020 Seong Tae Kim, Farrukh Mushtaq, Nassir Navab

Active learning is one of the solutions to this problem where an active learner is designed to indicate which samples need to be annotated to effectively train a target model.

Active Learning

Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning

3 code implementations30 Mar 2020 Hannes Hase, Mohammad Farid Azampour, Maria Tirindelli, Magdalini Paschali, Walter Simson, Emad Fatemizadeh, Nassir Navab

In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input.

Latent Patient Network Learning for Automatic Diagnosis

no code implementations27 Mar 2020 Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein

Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction.

Disease Prediction General Classification +1

TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks

1 code implementation24 Mar 2020 Tobias Czempiel, Magdalini Paschali, Matthias Keicher, Walter Simson, Hubertus Feussner, Seong Tae Kim, Nassir Navab

Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems.

From Perspective X-ray Imaging to Parallax-Robust Orthographic Stitching

no code implementations5 Mar 2020 Javad Fotouhi, Xingtong Liu, Mehran Armand, Nassir Navab, Mathias Unberath

Stitching images acquired under perspective projective geometry is a relevant topic in computer vision with multiple applications ranging from smartphone panoramas to the construction of digital maps.

Image Stitching

Spatiotemporal-Aware Augmented Reality: Redefining HCI in Image-Guided Therapy

no code implementations4 Mar 2020 Javad Fotouhi, Arian Mehrfard, Tianyu Song, Alex Johnson, Greg Osgood, Mathias Unberath, Mehran Armand, Nassir Navab

Suboptimal interaction with patient data and challenges in mastering 3D anatomy based on ill-posed 2D interventional images are essential concerns in image-guided therapies.

On the Effectiveness of Virtual Reality-based Training for Robotic Setup

no code implementations3 Mar 2020 Arian Mehrfard, Javad Fotouhi, Tess Forster, Giacomo Taylor, Danyal Fer, Deborah Nagle, Nassir Navab, Bernhard Fuerst

We trained 30 participants on how to set up a robotic arm in an environment mimicking clinical setup.


Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"

1 code implementation26 Feb 2020 Maria Tirindelli, Maria Victorova, Javier Esteban, Seong Tae Kim, David Navarro-Alarcon, Yong Ping Zheng, Nassir Navab

Processed force and ultrasound data are fused using a 1D Convolutional Network to compute the location of the vertebral levels.

Differentiable Graph Module (DGM) for Graph Convolutional Networks

no code implementations11 Feb 2020 Anees Kazi, Luca Cosmo, Nassir Navab, Michael Bronstein

In many settings, such as those arising in medical and healthcare applications, this assumption is not necessarily true since the graph may be noisy, partially- or even completely unknown, and one is thus interested in inferring it from the data.

Disease Prediction Point Cloud Segmentation +1

Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting

1 code implementation7 Feb 2020 Maxime Kayser, Roger D. Soberanis-Mukul, Anna-Maria Zvereva, Peter Klare, Nassir Navab, Shadi Albarqouni

We then investigated different strategies, such as a learning without forgetting framework, to leverage artifact knowledge to improve automated polyp detection.

Object Localization

A Comparative Analysis of Virtual Reality Head-Mounted Display Systems

no code implementations5 Dec 2019 Arian Mehrfard, Javad Fotouhi, Giacomo Taylor, Tess Forster, Nassir Navab, Bernhard Fuerst

With recent advances of Virtual Reality (VR) technology, the deployment of such will dramatically increase in non-entertainment environments, such as professional education and training, manufacturing, service, or low frequency/high risk scenarios.

Virtual Reality

Improving Feature Attribution through Input-specific Network Pruning

no code implementations25 Nov 2019 Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab

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.

Network Pruning

Radar Emitter Classification with Attribute-specific Recurrent Neural Networks

no code implementations18 Nov 2019 Paolo Notaro, Magdalini Paschali, Carsten Hopke, David Wittmann, Nassir Navab

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 classification.

Classification General Classification

Signal Clustering with Class-independent Segmentation

no code implementations18 Nov 2019 Stefano Gasperini, Magdalini Paschali, Carsten Hopke, David Wittmann, Nassir Navab

Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches.

Semantic Segmentation

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions

no code implementations20 Oct 2019 Tom Vercauteren, Mathias Unberath, Nicolas Padoy, Nassir Navab

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 interpretation and exploitation in clinical practice.

Decision Making

Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories

no code implementations19 Sep 2019 Jan-Nico Zaech, Cong Gao, Bastian Bier, Russell Taylor, Andreas Maier, Nassir Navab, Mathias Unberath

Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction.

Computed Tomography (CT)

Learn to Estimate Labels Uncertainty for Quality Assurance

no code implementations17 Sep 2019 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.

Bayesian Inference

Learn to Segment Organs with a Few Bounding Boxes

no code implementations17 Sep 2019 Abhijeet Parida, Arianne Tran, Nassir Navab, Shadi Albarqouni

Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology.

Semantic Segmentation

ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image

no code implementations ICCV 2019 Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari

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 semantic representations of the original and completed scene, all sharing the same latent space.

Object-Driven Multi-Layer Scene Decomposition From a Single Image

no code implementations ICCV 2019 Helisa Dhamo, Nassir Navab, Federico Tombari

Our approach aims at building up a Layered Depth Image (LDI) from a single RGB input, which is an efficient representation that arranges the scene in layers, including originally occluded regions.

RIO: 3D Object Instance Re-Localization in Changing Indoor Environments

no code implementations ICCV 2019 Johanna Wald, Armen Avetisyan, Nassir Navab, Federico Tombari, Matthias Nießner

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 in another 3D scan of the same environment taken at a later point in time.

Scene Understanding

Few-Shot Meta-Denoising

no code implementations31 Jul 2019 Leslie Casas, Attila Klimmek, Gustavo Carneiro, Nassir Navab, Vasileios Belagiannis

A solution to mitigate the small training set issue is to pre-train a denoising model with small training sets containing pairs of clean and synthesized noisy signals, produced from empirical noise priors, and fine-tune on the available small training set.

Denoising Few-Shot Learning +1

Reflective-AR Display: An Interaction Methodology for Virtual-Real Alignment in Medical Robotics

no code implementations23 Jul 2019 Javad Fotouhi, Tianyu Song, Arian Mehrfard, Giacomo Taylor, Qiaochu Wang, Fengfang Xian, Alejandro Martin-Gomez, Bernhard Fuerst, Mehran Armand, Mathias Unberath, Nassir Navab

To overcome this challenge, we introduce a novel registration concept for intuitive alignment of AR content to its physical counterpart by providing a multi-view AR experience via reflective-AR displays that simultaneously show the augmentations from multiple viewpoints.

Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

1 code implementation24 Jun 2019 Mohammad Eslami, Solale Tabarestani, Shadi Albarqouni, Ehsan Adeli, Nassir Navab, Malek Adjouadi

Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart.

Decision Making Translation

`Project & Excite' Modules for Segmentation of Volumetric Medical Scans

2 code implementations11 Jun 2019 Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua, Nassir Navab, Christian Wachinger

Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.

Brain Segmentation Semantic Segmentation

Uncertainty-based graph convolutional networks for organ segmentation refinement

1 code implementation5 Jun 2019 Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni

In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.

Graph Learning

Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer

no code implementations3 Jun 2019 Amal Lahiani, Nassir Navab, Shadi Albarqouni, Eldad Klaiman

Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images of virtually stained slide images in digital pathology with clinically validated interpretability.

Style Transfer

BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning

no code implementations16 May 2019 Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl, Nassir Navab, Christian Wachinger

A disadvantage of FL is the dependence on a central server, which requires all clients to agree on one trusted central body, and whose failure would disrupt the training process of all clients.

Brain Segmentation Federated Learning

Learning Interpretable Features via Adversarially Robust Optimization

no code implementations9 May 2019 Ashkan Khakzar, Shadi Albarqouni, Nassir Navab

In this work, we propose a method for improving the feature interpretability of neural network classifiers.

Decision Making

Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks

no code implementations8 May 2019 Hendrik Burwinkel, Anees Kazi, Gerome Vivar, Shadi Albarqouni, Guillaume Zahnd, Nassir Navab, Seyed-Ahmad Ahmadi

We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly.

Classification General Classification

Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets

no code implementations8 May 2019 Gerome Vivar, Hendrik Burwinkel, Anees Kazi, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi

Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal features.

Classification Decision Making +1

Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss

no code implementations18 Apr 2019 Mhd Hasan Sarhan, Shadi Albarqouni, Mehmet Yigitsoy, Nassir Navab, Abouzar Eslami

To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine.

Classification Diabetic Retinopathy Detection +1

Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations

no code implementations18 Apr 2019 Julia Rackerseder, Rüdiger Göbl, Nassir Navab, Christoph Hennersperger

Trained on the dataset alone, we report a Dice and Jaccard coefficient of $0. 45 \pm 0. 09$ and $0. 30 \pm 0. 07$ respectively, as well as an average distance of $0. 78 \pm 0. 36~mm$.

Collaboration Analysis Using Deep Learning

no code implementations17 Apr 2019 Zhang Guo, Kevin Yu, Rebecca Pearlman, Nassir Navab, Roghayeh Barmaki

The analysis of the collaborative learning process is one of the growing fields of education research, which has many different analytic solutions.

Object Recognition

Learning Interpretable Disentangled Representations using Adversarial VAEs

no code implementations17 Apr 2019 Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab, Shadi Albarqouni

Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice.

General Classification

Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound

no code implementations10 Apr 2019 Beatrice Demiray, Julia Rackerseder, Stevica Bozhinoski, Nassir Navab

We implement label transfer from MRI to US, which is prone to a residual but inevitable registration error.

Transfer Learning

End-to-End Learning-Based Ultrasound Reconstruction

no code implementations9 Apr 2019 Walter Simson, Rüdiger Göbl, Magdalini Paschali, Markus Krönke, Klemens Scheidhauer, Wolfgang Weber, Nassir Navab

The proposed method displays both promising image reconstruction quality and acquisition frequency when integrated for live ultrasound scanning.

Image Reconstruction

Adversarial Networks for Camera Pose Regression and Refinement

no code implementations15 Mar 2019 Mai Bui, Christoph Baur, Nassir Navab, Slobodan Ilic, Shadi Albarqouni

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 task.

Pose Estimation

LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices

1 code implementation9 Mar 2019 Laura Fink, Sing Chun Lee, Jie Ying Wu, Xingtong Liu, Tianyu Song, Yordanka Stoyanova, Marc Stamminger, Nassir Navab, Mathias Unberath

With the increasing computational power of today's workstations, real-time physically-based rendering is within reach, rapidly gaining attention across a variety of domains.

Data Visualization Image Generation

Semi-Supervised Few-Shot Learning with Prototypical Random Walks

1 code implementation6 Mar 2019 Ahmed Ayyad, Yuchen Li, Nassir Navab, Shadi Albarqouni, Mohamed Elhoseiny

We develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated.

Few-Shot Learning

Attention-based Lane Change Prediction

no code implementations4 Mar 2019 Oliver Scheel, Naveen Shankar Nagaraja, Loren Schwarz, Nassir Navab, Federico Tombari

Lane change prediction of surrounding vehicles is a key building block of path planning.

Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning

no code implementations4 Feb 2019 Amelia Jiménez-Sánchez, Anees Kazi, Shadi Albarqouni, Chlodwig Kirchhoff, Peter Biberthaler, Nassir Navab, Sonja Kirchhoff, Diana Mateus

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 images according to the AO classification.

Classification General Classification +2

Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness

no code implementations14 Jan 2019 Magdalini Paschali, Walter Simson, Abhijit Guha Roy, Muhammad Ferjad Naeem, Rüdiger Göbl, Christian Wachinger, Nassir Navab

Compared with traditional augmentation methods, and with images synthesized by Generative Adversarial Networks our method not only achieves state-of-the-art performance but also significantly improves the network's robustness.

Data Augmentation General Classification +2

Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits

no code implementations4 Jan 2019 Tolga Birdal, Benjamin Busam, Nassir Navab, Slobodan Ilic, Peter Sturm

Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points.

Self-Attention Equipped Graph Convolutions for Disease Prediction

no code implementations24 Dec 2018 Anees Kazi, S. Arvind krishna, Shayan Shekarforoush, Karsten Kortuem, Shadi Albarqouni, Nassir Navab

A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction.

Disease Prediction

Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data

no code implementations ICCV 2019 Fabian Manhardt, Diego Martin Arroyo, Christian Rupprecht, Benjamin Busam, Tolga Birdal, Nassir Navab, Federico Tombari

For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures.

3D Object Detection Pose Estimation

Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control

2 code implementations24 Nov 2018 Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger

Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control.

Brain Segmentation

Redefining Ultrasound Compounding: Computational Sonography

no code implementations5 Nov 2018 Rüdiger Göbl, Diana Mateus, Christoph Hennersperger, Maximilian Baust, Nassir Navab

By providing a novel paradigm for the acquisition and reconstruction of tracked freehand 3D ultrasound, this work presents the concept of Computational Sonography (CS) to model the directionality of ultrasound information.

Dealing with Ambiguity in Robotic Grasping via Multiple Predictions

no code implementations2 Nov 2018 Ghazal Ghazaei, Iro Laina, Christian Rupprecht, Federico Tombari, Nassir Navab, Kianoush Nazarpour

Further, we reformulate the problem of robotic grasping by replacing conventional grasp rectangles with grasp belief maps, which hold more precise location information than a rectangle and account for the uncertainty inherent to the task.

Robotic Grasping

Adversarial Semantic Scene Completion from a Single Depth Image

no code implementations25 Oct 2018 Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari

We propose a method to reconstruct, complete and semantically label a 3D scene from a single input depth image.

Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach

no code implementations15 Oct 2018 Amal Lahiani, Jacob Gildenblat, Irina Klaman, Shadi Albarqouni, Nassir Navab, Eldad Klaiman

Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology.

InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation

no code implementations11 Oct 2018 Shubham Kumar, Sailesh Conjeti, Abhijit Guha Roy, Christian Wachinger, Nassir Navab

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 images at iso-intense stage, which can be easily extended for other segmentation tasks involving multi-modalities.

Infant Brain Mri Segmentation MRI segmentation +1

GANs for Medical Image Analysis

no code implementations13 Sep 2018 Salome Kazeminia, Christoph Baur, Arjan Kuijper, Bram van Ginneken, Nassir Navab, Shadi Albarqouni, Anirban Mukhopadhyay

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 image de-noising, reconstruction, segmentation, data simulation, detection or classification.

General Classification

Generating Highly Realistic Images of Skin Lesions with GANs

no code implementations5 Sep 2018 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 problems for the training of robust and well-generalizing models.

Lesion Segmentation

Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images

no code implementations ECCV 2018 Keisuke Tateno, Nassir Navab, Federico Tombari

There is a high demand of 3D data for 360° panoramic images and videos, pushed by the growing availability on the market of specialized hardware for both capturing (e. g., omnidirectional cameras) as well as visualizing in 3D (e. g., head mounted displays) panoramic images and videos.

Semantic Segmentation Style Transfer

Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks

5 code implementations23 Aug 2018 Abhijit Guha Roy, Nassir Navab, Christian Wachinger

Towards this end, we introduce three variants of SE modules for segmentation, (i) squeezing spatially and exciting channel-wise, (ii) squeezing channel-wise and exciting spatially and (iii) joint spatial and channel 'squeeze & excitation'.

Image Classification Semantic Segmentation

Fully-Convolutional Point Networks for Large-Scale Point Clouds

1 code implementation ECCV 2018 Dario Rethage, Johanna Wald, Jürgen Sturm, Nassir Navab, Federico Tombari

This work proposes a general-purpose, fully-convolutional network architecture for efficiently processing large-scale 3D data.

Semantic Segmentation

Human Motion Analysis with Deep Metric Learning

1 code implementation ECCV 2018 Huseyin Coskun, David Joseph Tan, Sailesh Conjeti, Nassir Navab, Federico Tombari

Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks.

Dynamic Time Warping Metric Learning

Peeking Behind Objects: Layered Depth Prediction from a Single Image

no code implementations23 Jul 2018 Helisa Dhamo, Keisuke Tateno, Iro Laina, Nassir Navab, Federico Tombari

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.

Depth Estimation Virtual Reality

Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks

no code implementations20 Jul 2018 Santiago Estrada, Sailesh Conjeti, Muneer Ahmad, Nassir Navab, Martin Reuter

Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance for semantic segmentation.

Semantic Segmentation

SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis

no code implementations29 Jun 2018 Deepa Gunashekar, Sailesh Conjeti, Abhijit Guha Roy, Nassir Navab, Kuangyu Shi

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 to MR, MR to CT, CT to PET etc, without the need for an actual acquisition. Though they show potential for applications in radiation therapy planning, image super resolution, atlas construction, image segmentation etc. The synthesis results are not as accurate as the actual acquisition. In this paper, we address the problem of multi modal image synthesis by proposing a fully convolutional deep learning architecture called the SynNet. We extend the proposed architecture for various input output configurations.

Image Generation Image Super-Resolution +1

Augmented Reality-based Feedback for Technician-in-the-loop C-arm Repositioning

no code implementations22 Jun 2018 Mathias Unberath, Javad Fotouhi, Jonas Hajek, Andreas Maier, Greg Osgood, Russell Taylor, Mehran Armand, Nassir Navab

For C-arm repositioning to a particular target view, the recorded C-arm pose is restored as a virtual object and visualized in an AR environment, serving as a perceptual reference for the technician.

Machine learning-based colon deformation estimation method for colonoscope tracking

no code implementations8 Jun 2018 Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku MORI

An estimation method of colon deformations occur during colonoscope insertions is necessary to reduce tracking errors.

CFCM: Segmentation via Coarse to Fine Context Memory

1 code implementation4 Jun 2018 Fausto Milletari, Nicola Rieke, Maximilian Baust, Marco Esposito, Nassir Navab

Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales.

Semantic Segmentation

Scene Coordinate and Correspondence Learning for Image-Based Localization

no code implementations22 May 2018 Mai Bui, Shadi Albarqouni, Slobodan Ilic, Nassir Navab

Scene coordinate regression has become an essential part of current camera re-localization methods.

Image-Based Localization

Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction

no code implementations17 May 2018 Oliver Scheel, Loren Schwarz, Nassir Navab, Federico Tombari

One of the greatest challenges towards fully autonomous cars is the understanding of complex and dynamic scenes.

Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks

no code implementations17 May 2018 Amal Lahiani, Jacob Gildenblat, Irina Klaman, Nassir Navab, Eldad Klaiman

A key challenge in cancer immunotherapy biomarker research is quantification of pattern changes in microscopic whole slide images of tumor biopsies.

whole slide images

When Regression Meets Manifold Learning for Object Recognition and Pose Estimation

no code implementations16 May 2018 Mai Bui, Sergey Zakharov, Shadi Albarqouni, Slobodan Ilic, Nassir Navab

By combining the strengths of manifold learning using triplet loss and pose regression, we could either estimate the pose directly reducing the complexity compared to NN search, or use learned descriptor for the NN descriptor matching.

Multi-Task Learning Object Recognition +2

Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction

no code implementations28 Apr 2018 Anees Kazi, Shadi Albarqouni, Karsten Kortuem, Nassir Navab

Structural data from Electronic Health Records as complementary information to imaging data for disease prediction.

Disease Prediction

Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound

no code implementations20 Apr 2018 Markus A. Degel, Nassir Navab, Shadi Albarqouni

Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions.

Domain Adaptation Left Atrium Segmentation

Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling

no code implementations19 Apr 2018 Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger

We introduce inherent measures for effective quality control of brain segmentation based on a Bayesian fully convolutional neural network, using model uncertainty.

Brain Segmentation Translation

MelanoGANs: High Resolution Skin Lesion Synthesis with GANs

no code implementations12 Apr 2018 Christoph Baur, Shadi Albarqouni, Nassir Navab

Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking images of faces, scenery and even medical images.

Image Generation Lesion Classification +1

Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images

1 code implementation12 Apr 2018 Christoph Baur, Benedikt Wiestler, Shadi Albarqouni, Nassir Navab

Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images.

Representation Learning Unsupervised Anomaly Detection

Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions

no code implementations9 Apr 2018 Javad Fotouhi, Mathias Unberath, Giacomo Taylor, Arash Ghaani Farashahi, Bastian Bier, Russell H. Taylor, Greg M. Osgood, M. D., Mehran Armand, Nassir Navab

The main challenge is to automatically estimate the desired plane of symmetry within the patient's pre-operative CT. We propose to estimate this plane using a non-linear optimization strategy, by minimizing Tukey's biweight robust estimator, relying on the partial symmetry of the anatomy.

Image Augmentation

Webly Supervised Learning for Skin Lesion Classification

no code implementations31 Mar 2018 Fernando Navarro, Sailesh Conjeti, Federico Tombari, Nassir Navab

Within medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive.

Classification General Classification +3

Guide Me: Interacting with Deep Networks

no code implementations CVPR 2018 Christian Rupprecht, Iro Laina, Nassir Navab, Gregory D. Hager, Federico Tombari

Interaction and collaboration between humans and intelligent machines has become increasingly important as machine learning methods move into real-world applications that involve end users.

Image Captioning Image Generation

Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion

no code implementations30 Mar 2018 Gerome Vivar, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi

In this work, we follow up on the idea of modeling multi-modal disease classification as a matrix completion problem, with simultaneous classification and non-linear imputation of features.

Classification General Classification +2

Generalizability vs. Robustness: Adversarial Examples for Medical Imaging

no code implementations23 Mar 2018 Magdalini Paschali, Sailesh Conjeti, Fernando Navarro, Nassir Navab

In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data.

Brain Segmentation General Classification +2

DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures

2 code implementations22 Mar 2018 Mathias Unberath, Jan-Nico Zaech, Sing Chun Lee, Bastian Bier, Javad Fotouhi, Mehran Armand, Nassir Navab

Machine learning-based approaches outperform competing methods in most disciplines relevant to diagnostic radiology.

Domain Adaptation