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Found 365 papers, 117 papers with code
3D Pictorial Structures for Multiple Human Pose Estimation
In this work, we address the problem of 3D pose estimation of multiple humans from multiple views.
Ranked #24 on
3D Multi-Person Pose Estimation
on Shelf
Human Shape and Pose Tracking Using Keyframes
In contrast to many existing approaches that rely on a single reference model, multiple templates represent a larger variability of human poses.
Multiple human pose estimation with temporally consistent 3d pictorial structures
To address these challenges, we propose a temporally consistent 3D Pictorial Structures model (3DPS) for multiple human pose estimation from multiple camera views.
Ranked #16 on
3D Multi-Person Pose Estimation
on Campus
Robust Optimization for Deep Regression
Convolutional Neural Networks (ConvNets) have successfully contributed to improve the accuracy of regression-based methods for computer vision tasks such as human pose estimation, landmark localization, and object detection.
Total Variation Regularization of Shape Signals
This paper introduces the concept of shape signals, i. e., series of shapes which have a natural temporal or spatial ordering, as well as a variational formulation for the regularization of these signals.
Toward User-Specific Tracking by Detection of Human Shapes in Multi-Cameras
It usually comprises an association step, that finds correspondences between the model and the input data, and a deformation step, that fits the model to the observations given correspondences.
Image Segmentation in Twenty Questions
Consider the following scenario between a human user and the computer.
Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests
This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data.
Weakly-Supervised Structured Output Learning With Flexible and Latent Graphs Using High-Order Loss Functions
We introduce two new structured output models that use a latent graph, which is flexible in terms of the number of nodes and structure, where the training process minimises a high-order loss function using a weakly annotated training set.
A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online
This paper proposes a temporal tracking algorithm based on Random Forest that uses depth images to estimate and track the 3D pose of a rigid object in real-time.
Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound
In this work we propose a novel approach to perform segmentation by leveraging the abstraction capabilities of convolutional neural networks (CNNs).
Deeper Depth Prediction with Fully Convolutional Residual Networks
This paper addresses the problem of estimating the depth map of a scene given a single RGB image.
Volumetric 3D Tracking by Detection
In this paper, we propose a new framework for 3D tracking by detection based on fully volumetric representations.
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
A Taxonomy and Library for Visualizing Learned Features in Convolutional Neural Networks
Over the last decade, Convolutional Neural Networks (CNN) saw a tremendous surge in performance.
Deep Active Contours
We propose a method for interactive boundary extraction which combines a deep, patch-based representation with an active contour framework.
Hashmod: A Hashing Method for Scalable 3D Object Detection
We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data.
Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation
We present a 3D object detection method that uses regressed descriptors of locally-sampled RGB-D patches for 6D vote casting.
Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields
A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually.
An Octree-Based Approach towards Efficient Variational Range Data Fusion
Volume-based reconstruction is usually expensive both in terms of memory consumption and runtime.
A Deep Metric for Multimodal Registration
Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities.
Hands-Free Segmentation of Medical Volumes via Binary Inputs
We propose a novel hands-free method to interactively segment 3D medical volumes.
3D Pictorial Structures Revisited: Multiple Human Pose Estimation
We address the problem of 3D pose estimation of multiple humans from multiple views.
Ranked #15 on
3D Multi-Person Pose Estimation
on Campus
The TUM LapChole dataset for the M2CAI 2016 workflow challenge
In this technical report we present our collected dataset of laparoscopic cholecystectomies (LapChole).
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support.
Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses
In future prediction, for example, many distinct outcomes are equally valid.
Deep Residual Hashing
Hashing aims at generating highly compact similarity preserving code words which are well suited for large-scale image retrieval tasks.
Cross-Modal Manifold Learning for Cross-modal Retrieval
This paper presents a new scalable algorithm for cross-modal similarity preserving retrieval in a learnt manifold space.
X-ray In-Depth Decomposition: Revealing The Latent Structures
X-ray radiography is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics, and trauma procedures.
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies
Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to capture long-term dependencies remains difficult.
Learning Robust Hash Codes for Multiple Instance Image Retrieval
In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for large-scale retrieval.
Semi-Supervised Deep Learning for Fully Convolutional Networks
Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious.
Deep Residual Learning for Instrument Segmentation in Robotic Surgery
Detection, tracking, and pose estimation of surgical instruments are crucial tasks for computer assistance during minimally invasive robotic surgery.
Concurrent Segmentation and Localization for Tracking of Surgical Instruments
Real-time instrument tracking is a crucial requirement for various computer-assisted interventions.
ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network
Optical coherence tomography (OCT) is used for non-invasive diagnosis of diabetic macular edema assessing the retinal layers.
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction.
Camera Pose Filtering with Local Regression Geodesics on the Riemannian Manifold of Dual Quaternions
Time-varying, smooth trajectory estimation is of great interest to the vision community for accurate and well behaving 3D systems.
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited.
Learning-based Surgical Workflow Detection from Intra-Operative Signals
A modern operating room (OR) provides a plethora of advanced medical devices.
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
In this paper, we propose a method for automatic segmentation of ICAC; the first to our knowledge.
Long Short-Term Memory Kalman Filters:Recurrent Neural Estimators for Pose Regularization
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization.
6D Object Pose Estimation with Depth Images: A Seamless Approach for Robotic Interaction and Augmented Reality
To determine the 3D orientation and 3D location of objects in the surroundings of a camera mounted on a robot or mobile device, we developed two powerful algorithms in object detection and temporal tracking that are combined seamlessly for robotic perception and interaction as well as Augmented Reality (AR).
Multi-layer Visualization for Medical Mixed Reality
In this paper and to our knowledge, we propose a multi-layer visualization in Medical Mixed Reality solution which subtly improves a surgeon's visualization by making transparent the occluding objects.
Long Short-Term Memory Kalman Filters: Recurrent Neural Estimators for Pose Regularization
One-shot pose estimation for tasks such as body joint localization, camera pose estimation, and object tracking are generally noisy, and temporal filters have been extensively used for regularization.
SUPRA: Open Source Software Defined Ultrasound Processing for Real-Time Applications
Including all processing stages of a usual ultrasound pipeline, the run-time analysis shows that it can be executed in 2D and 3D on consumer GPUs in real-time.
SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot.
Ranked #1 on
6D Pose Estimation using RGBD
on Tejani
On-the-fly Augmented Reality for Orthopaedic Surgery Using a Multi-Modal Fiducial
Then, annotations on the 2D X-ray images can be rendered as virtual objects in 3D providing surgical guidance.
Plan in 2D, execute in 3D: An augmented reality solution for cup placement in total hip arthroplasty
Reproducibly achieving proper implant alignment is a critical step in total hip arthroplasty (THA) procedures that has been shown to substantially affect patient outcome.
QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy
We introduce QuickNAT, a fully convolutional, densely connected neural network that segments a \revision{MRI brain scan} in 20 seconds.
Fast 5DOF Needle Tracking in iOCT
We propose to build on its desirable qualities and present a method for tracking the orientation and location of a surgical needle.
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.
A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds
As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure.
Closing the Calibration Loop: An Inside-out-tracking Paradigm for Augmented Reality in Orthopedic Surgery
In percutaneous orthopedic interventions the surgeon attempts to reduce and fixate fractures in bony structures.
DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures
Machine learning-based approaches outperform competing methods in most disciplines relevant to diagnostic radiology.
X-ray-transform Invariant Anatomical Landmark Detection for Pelvic Trauma Surgery
In this work, we present a method to automatically detect anatomical landmarks in X-ray images independent of the viewing direction.
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging
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.
Guide Me: Interacting with Deep Networks
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.
Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion
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.
Webly Supervised Learning for Skin Lesion Classification
Within medical imaging, manual curation of sufficient well-labeled samples is cost, time and scale-prohibitive.
StainGAN: Stain Style Transfer for Digital Histological Images
Digitized Histological diagnosis is in increasing demand.
Markerless Inside-Out Tracking for Interventional Applications
Tracking of rotation and translation of medical instruments plays a substantial role in many modern interventions.
Exploiting Partial Structural Symmetry For Patient-Specific Image Augmentation in Trauma Interventions
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.
Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images.
MelanoGANs: High Resolution Skin Lesion Synthesis with GANs
Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking images of faces, scenery and even medical images.
Inherent Brain Segmentation Quality Control from Fully ConvNet Monte Carlo Sampling
We introduce inherent measures for effective quality control of brain segmentation based on a Bayesian fully convolutional neural network, using model uncertainty.
Domain and Geometry Agnostic CNNs for Left Atrium Segmentation in 3D Ultrasound
Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions.
Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction
Structural data from Electronic Health Records as complementary information to imaging data for disease prediction.
When Regression Meets Manifold Learning for Object Recognition and Pose Estimation
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.
Situation Assessment for Planning Lane Changes: Combining Recurrent Models and Prediction
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
A key challenge in cancer immunotherapy biomarker research is quantification of pattern changes in microscopic whole slide images of tumor biopsies.
Scene Coordinate and Correspondence Learning for Image-Based Localization
Scene coordinate regression has become an essential part of current camera re-localization methods.
CFCM: Segmentation via Coarse to Fine Context Memory
Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales.
Machine learning-based colon deformation estimation method for colonoscope tracking
An estimation method of colon deformations occur during colonoscope insertions is necessary to reduce tracking errors.
Initialize globally before acting locally: Enabling Landmark-free 3D US to MRI Registration
Registration of partial-view 3D US volumes with MRI data is influenced by initialization.
Augmented Reality-based Feedback for Technician-in-the-loop C-arm Repositioning
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.
SynNet: Structure-Preserving Fully Convolutional Networks for Medical Image Synthesis
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.
Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks
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.
Peeking Behind Objects: Layered Depth Prediction from a Single Image
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.
Human Motion Analysis with Deep Metric Learning
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.
Towards Robotic Eye Surgery: Marker-free, Online Hand-eye Calibration using Optical Coherence Tomography Images
Ophthalmic microsurgery is known to be a challenging operation, which requires very precise and dexterous manipulation.
Fully-Convolutional Point Networks for Large-Scale Point Clouds
This work proposes a general-purpose, fully-convolutional network architecture for efficiently processing large-scale 3D data.
Ranked #27 on
Semantic Segmentation
on ScanNet
Deep Learned Full-3D Object Completion from Single View
3D geometry is a very informative cue when interacting with and navigating an environment.
Recalibrating Fully Convolutional Networks with Spatial and Channel 'Squeeze & Excitation' Blocks
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'.
Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images
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.
Ranked #10 on
Depth Estimation
on Stanford2D3D Panoramic
Generating Highly Realistic Images of Skin Lesions with GANs
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.
GANs for Medical Image Analysis
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.
Weakly-Supervised Localization and Classification of Proximal Femur Fractures
In this paper, we target the problem of fracture classification from clinical X-Ray images towards an automated Computer Aided Diagnosis (CAD) system.
Deep Model-Based 6D Pose Refinement in RGB
We present a novel approach for model-based 6D pose refinement in color data.
InfiNet: Fully Convolutional Networks for Infant Brain MRI Segmentation
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.
Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach
Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology.
The speaker-independent lipreading play-off; a survey of lipreading machines
Lipreading is a difficult gesture classification task.
Adversarial Semantic Scene Completion from a Single Depth Image
We propose a method to reconstruct, complete and semantically label a 3D scene from a single input depth image.
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions
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.
Redefining Ultrasound Compounding: Computational Sonography
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.
Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Control
Next to voxel-wise uncertainty, we introduce four metrics to quantify structure-wise uncertainty in segmentation for quality control.
Explaining the Ambiguity of Object Detection and 6D Pose From Visual Data
For each object instance we predict multiple pose and class outcomes to estimate the specific pose distribution generated by symmetries and repetitive textures.
Adversarial Signal Denoising with Encoder-Decoder Networks
The presence of noise is common in signal processing regardless the signal type.
Self-Attention Equipped Graph Convolutions for Disease Prediction
A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction.
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits
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.
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness
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.
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
This representation is passed on to the segmenter arm that uses this information to segment the new query image.
Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning
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.
Attention-based Lane Change Prediction
Lane change prediction of surrounding vehicles is a key building block of path planning.
Semi-Supervised Few-Shot Learning with Prototypical Random Walks
We develop a random walk semi-supervised loss that enables the network to learn representations that are compact and well-separated.
LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices
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.
InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain.
Adversarial Networks for Camera Pose Regression and Refinement
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.
Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks
In this paper, we propose a novel interpretation method tailored to histological Whole Slide Image (WSI) processing.
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements.
End-to-End Learning-Based Ultrasound Reconstruction
The proposed method displays both promising image reconstruction quality and acquisition frequency when integrated for live ultrasound scanning.
Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound
We implement label transfer from MRI to US, which is prone to a residual but inevitable registration error.
Learning Interpretable Disentangled Representations using Adversarial VAEs
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice.
Collaboration Analysis Using Deep Learning
The analysis of the collaborative learning process is one of the growing fields of education research, which has many different analytic solutions.
Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations
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 $0. 78 \pm 0. 36~mm$.
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss
To enhance the discriminative power of the classification model, we incorporate triplet embedding loss with a selective sampling routine.
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
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.
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks
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.
Learning Interpretable Features via Adversarially Robust Optimization
In this work, we propose a method for improving the feature interpretability of neural network classifiers.
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
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.
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
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.
Uncertainty-based graph convolutional networks for organ segmentation refinement
In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging.
Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography
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.
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition
The lack of large-scale real datasets with annotations makes transfer learning a necessity for video activity understanding.
Reflective-AR Display: An Interaction Methodology for Virtual-Real Alignment in Medical Robotics
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.
Few-Shot Meta-Denoising
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.
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation
We present a sampling-free approach for computing the epistemic uncertainty of a neural network.
Grasp Type Estimation for Myoelectric Prostheses using Point Cloud Feature Learning
Prosthetic hands can help people with limb difference to return to their life routines.
RIO: 3D Object Instance Re-Localization in Changing Indoor Environments
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.
Towards Unsupervised Image Captioning with Shared Multimodal Embeddings
The core component of our approach is a shared latent space that is structured by visual concepts.
Object-Driven Multi-Layer Scene Decomposition From a Single Image
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.
ForkNet: Multi-branch Volumetric Semantic Completion from a Single Depth Image
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.
Ranked #7 on
3D Semantic Scene Completion
on NYUv2
(using extra training data)
Learn to Segment Organs with a Few Bounding Boxes
Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology.
Learn to Estimate Labels Uncertainty for Quality Assurance
Deep Learning sets the state-of-the-art in many challenging tasks showing outstanding performance in a broad range of applications.
Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories
Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction.
CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions
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.
Radar Emitter Classification with Attribute-specific Recurrent Neural Networks
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.
Signal Clustering with Class-independent Segmentation
Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches.
Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core
We present a novel method to track 3D models in color and depth data.
Improving Feature Attribution through Input-specific Network Pruning
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.
A Comparative Analysis of Virtual Reality Head-Mounted Display Systems
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.
Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting
We then investigated different strategies, such as a learning without forgetting framework, to leverage artifact knowledge to improve automated polyp detection.
Differentiable Graph Module (DGM) for Graph Convolutional Networks
We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning).
Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"
Processed force and ultrasound data are fused using a 1D Convolutional Network to compute the location of the vertebral levels.
On the Effectiveness of Virtual Reality-based Training for Robotic Setup
We trained 30 participants on how to set up a robotic arm in an environment mimicking clinical setup.
Robotics
Spatiotemporal-Aware Augmented Reality: Redefining HCI in Image-Guided Therapy
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.
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging
We present a novel methodology to detect imperfect bilateral symmetry in CT of human anatomy.
From Perspective X-ray Imaging to Parallax-Robust Orthographic Stitching
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.
Fairness by Learning Orthogonal Disentangled Representations
We explicitly enforce the meaningful representation to be agnostic to sensitive information by entropy maximization.
CPS++: Improving Class-level 6D Pose and Shape Estimation From Monocular Images With Self-Supervised Learning
Contemporary monocular 6D pose estimation methods can only cope with a handful of object instances.
ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
Medical image segmentation is one of the major challenges addressed by machine learning methods.
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
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.
Ranked #4 on
Surgical phase recognition
on Cholec80
Latent-Graph Learning for Disease Prediction
Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction.
Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input.
Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time
Computer-aided diagnosis (CADx) algorithms in medicine provide patient-specific decision support for physicians.
Medical-based Deep Curriculum Learning for Improved Fracture Classification
Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge.
Confident Coreset for Active Learning in Medical Image Analysis
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.
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI.
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation.
Semantic Image Manipulation Using Scene Graphs
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.
Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions
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.
Ranked #3 on
3d scene graph generation
on 3DSSG
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.
Self6D: Self-Supervised Monocular 6D Object Pose Estimation
6D object pose estimation is a fundamental problem in computer vision.
Colonoscope tracking method based on shape estimation network
We utilize the shape estimation network (SEN), which estimates deformed colon shape during colonoscope insertions.
Colon Shape Estimation Method for Colonoscope Tracking using Recurrent Neural Networks
We propose a colon deformation estimation method using RNN and obtain the colonoscope shape from electromagnetic sensors during its insertion into the colon.
Explicit Domain Adaptation with Loosely Coupled Samples
In this work we propose a transfer learning framework, core of which is learning an explicit mapping between domains.
Decision Support for Intoxication Prediction Using Graph Convolutional Networks
Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame.
Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions
The cataract, a developing opacity of the human eye lens, constitutes the world's most frequent cause for blindness.
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
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).
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
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.
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
Brain pathologies can vary greatly in size and shape, ranging from few pixels (i. e. MS lesions) to large, space-occupying tumors.
Augment Yourself: Mixed Reality Self-Augmentation Using Optical See-through Head-mounted Displays and Physical Mirrors
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.
Searching for Efficient Architecture for Instrument Segmentation in Robotic Surgery
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.
Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks
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.
Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis.
DynaMiTe: A Dynamic Local Motion Model with Temporal Constraints for Robust Real-Time Feature Matching
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.
Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty
In this paper, we propose a method for the automatic classification of proximal femur fractures into 3 and 7 AO classes based on a Convolutional Neural Network (CNN).
Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments
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.
Robotics
Continual Class Incremental Learning for CT Thoracic Segmentation
In this work, we evaluate FT and LwF for class incremental learning in multi-organ segmentation using the publicly available AAPM dataset.
A Learning-based Method for Online Adjustment of C-arm Cone-Beam CT Source Trajectories for Artifact Avoidance
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.
Inverse Distance Aggregation for Federated Learning with Non-IID Data
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years.
SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification
In this paper, we propose a method for 3D object completion and classification based on point clouds.
Polyp-artifact relationship analysis using graph inductive learned representations
In inference, we use this classifier to analyze a second graph, generated from artifact and polyp predictions given by region proposal networks.
I Like to Move It: 6D Pose Estimation as an Action Decision Process
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.
RGB-D SLAM with Structural Regularities
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.
Robotics
Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration
3D Point clouds are a rich source of information that enjoy growing popularity in the vision community.
SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion
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.
Panoster: End-to-end Panoptic Segmentation of LiDAR Point Clouds
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.
Surgical Data Science -- from Concepts toward Clinical Translation
We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
Self-Supervised Out-of-Distribution Detection in Brain CT Scans
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.
Pose-dependent weights and Domain Randomization for fully automatic X-ray to CT Registration
First, a neural network is trained once to detect a set of anatomical landmarks on simulated X-rays.
Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods
In this work, we empirically show that two approaches for handling the gradient information, namely positive aggregation, and positive propagation, break these methods.
An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
In this context, we proposed a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation
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.
Meta-k: Towards Unsupervised Prediction of Number of Clusters
Data clustering is a well-known unsupervised learning approach.
On Sparse Critical Paths of Neural Response
Is critical input information encoded in specific sparse paths within the network?
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
This is accomplished by associating a graph-based neural network to each class, which is responsible for weighting the class samples and changing the importance of each sample for the classifier.
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences.
Semi-Supervised Federated Peer Learning for Skin Lesion Classification
With few annotated data, FedPerl is on par with a state-of-the-art method in skin lesion classification in the standard setup while outperforming SSFLs and the baselines by 1. 8% and 15. 8%, respectively.
Are we using appropriate segmentation metrics? Identifying correlates of human expert perception for CNN training beyond rolling the DICE coefficient
It is often unclear how to optimize abstract metrics, such as human expert perception, in convolutional neural network (CNN) training.
