Search Results for author:
Found 346 papers, 108 papers with code
RaDialog: A Large Vision-Language Model for Radiology Report Generation and Conversational Assistance
Conversational AI tools that can generate and discuss clinically correct radiology reports for a given medical image have the potential to transform radiology.
Robust Tumor Segmentation with Hyperspectral Imaging and Graph Neural Networks
Despite the limited dataset, the GNN-based model significantly outperforms context-agnostic approaches, accurately distinguishing between healthy and tumor tissues, even in images from previously unseen patients.
SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation
These geometric features are then point-aligned with DINOv2 features to establish a consistent object representation under SE(3) transformations, facilitating the mapping from camera space to the pre-defined canonical space, thus further enhancing pose estimation.
EyeLS: Shadow-Guided Instrument Landing System for Intraocular Target Approaching in Robotic Eye Surgery
Robotic ophthalmic surgery is an emerging technology to facilitate high-precision interventions such as retina penetration in subretinal injection and removal of floating tissues in retinal detachment depending on the input imaging modalities such as microscopy and intraoperative OCT (iOCT).
VoxNeRF: Bridging Voxel Representation and Neural Radiance Fields for Enhanced Indoor View Synthesis
Creating high-quality view synthesis is essential for immersive applications but continues to be problematic, particularly in indoor environments and for real-time deployment.
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation
We take advantage of the outer part of the masked area as they have a direct correlation with the context of the scene.
AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers
To date, endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature.
Dynamic Scene Graph Representation for Surgical Video
Surgical videos captured from microscopic or endoscopic imaging devices are rich but complex sources of information, depicting different tools and anatomical structures utilized during an extended amount of time.
SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs
In this paper, we present SG-Bot, a novel rearrangement framework that utilizes a coarse-to-fine scheme with a scene graph as the scene representation.
RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy
While most classical methods achieve rotation-equivariant detection and invariant description by design, many learning-based approaches learn to be robust only up to a certain degree.
DynaMoN: Motion-Aware Fast And Robust Camera Localization for Dynamic NeRF
Dynamic reconstruction with neural radiance fields (NeRF) requires accurate camera poses.
Dynamic Hyperbolic Attention Network for Fine Hand-object Reconstruction
In this work, we propose the first precise hand-object reconstruction method in hyperbolic space, namely Dynamic Hyperbolic Attention Network (DHANet), which leverages intrinsic properties of hyperbolic space to learn representative features.
BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior
Light-sheet fluorescence microscopy (LSFM), a planar illumination technique that enables high-resolution imaging of samples, experiences defocused image quality caused by light scattering when photons propagate through thick tissues.
On the Localization of Ultrasound Image Slices within Point Distribution Models
We demonstrate that our multi-modal registration framework can localize images on the 3D surface topology of a patient-specific organ and the mean shape of an SSM.
3D Adversarial Augmentations for Robust Out-of-Domain Predictions
We conduct extensive experiments across a variety of scenarios on data from KITTI, Waymo, and CrashD for 3D object detection, and on data from SemanticKITTI, Waymo, and nuScenes for 3D semantic segmentation.
A Continual Learning Approach for Cross-Domain White Blood Cell Classification
Here, we propose a rehearsal-based continual learning approach for class incremental and domain incremental scenarios in white blood cell classification.
A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes
To that end, we train multiple MIL models using different levels of sex imbalance in the training set and excluding certain age groups.
Multi-Modal Dataset Acquisition for Photometrically Challenging Object
This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects.
Polarimetric Information for Multi-Modal 6D Pose Estimation of Photometrically Challenging Objects with Limited Data
6D pose estimation pipelines that rely on RGB-only or RGB-D data show limitations for photometrically challenging objects with e. g. textureless surfaces, reflections or transparency.
Can ultrasound confidence maps predict sonographers' labeling variability?
Therefore, we propose to give the confidence maps as additional information to the networks.
Robust Monocular Depth Estimation under Challenging Conditions
While state-of-the-art monocular depth estimation approaches achieve impressive results in ideal settings, they are highly unreliable under challenging illumination and weather conditions, such as at nighttime or in the presence of rain.
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks
Leveraging this, we introduce DISBELIEVE, a local model poisoning attack that creates malicious parameters or gradients such that their distance to benign clients' parameters or gradients is low respectively but at the same time their adverse effect on the global model's performance is high.
WarpEM: Dynamic Time Warping for Accurate Catheter Registration in EM-guided Procedures
Accurate catheter tracking is crucial during minimally invasive endovascular procedures (MIEP), and electromagnetic (EM) tracking is a widely used technology that serves this purpose.
DefCor-Net: Physics-Aware Ultrasound Deformation Correction
The recovery of morphologically accurate anatomical images from deformed ones is challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent diagnosis, particularly in the emerging field of computer-assisted diagnosis.
LOTUS: Learning to Optimize Task-based US representations
Anatomical segmentation of organs in ultrasound images is essential to many clinical applications, particularly for diagnosis and monitoring.
Learning Multi-modal Representations by Watching Hundreds of Surgical Video Lectures
SurgVLP constructs a new contrastive learning objective to align video clip embeddings with the corresponding multiple text embeddings by bringing them together within a joint latent space.
DisguisOR: Holistic Face Anonymization for the Operating Room
Methods: RGB and depth images from multiple cameras are fused into a 3D point cloud representation of the scene.
DISA: DIfferentiable Similarity Approximation for Universal Multimodal Registration
Our method is several orders of magnitude faster than local patch-based metrics and can be directly applied in clinical settings by replacing the similarity measure with the proposed one.
Rad-ReStruct: A Novel VQA Benchmark and Method for Structured Radiology Reporting
However, there is limited research on automating structured reporting, and no public benchmark is available for evaluating and comparing different methods.
Ranked #1 on
Structured Report Generation
on Rad-ReStruct
Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations
The results demonstrated that the proposed advanced framework can robustly work on a variety of seen and unseen phantoms as well as in-vivo human carotid data.
Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph
To address this challenge, a graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup by explicitly considering subcutaneous bone surface features instead of the skin surface.
Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation
To validate the proposed robotic US system for imaging arteries, experiments are carried out on volunteers' carotid and radial arteries.
AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection
This work intends to, first, propose a robust inpainting model to learn the details of healthy anatomies and reconstruct high-resolution images by preserving anatomical constraints.
Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET
Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes.
DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal
The experimental results demonstrate that the proposed approach with the re-identification process can significantly improve the accuracy and robustness of the segmentation results (dice score: from 0:54 to 0:86; intersection over union: from 0:47 to 0:78).
Skeleton Graph-based Ultrasound-CT Non-rigid Registration
Autonomous ultrasound (US) scanning has attracted increased attention, and it has been seen as a potential solution to overcome the limitations of conventional US examinations, such as inter-operator variations.
Next-generation Surgical Navigation: Multi-view Marker-less 6DoF Pose Estimation of Surgical Instruments
A particular focus in computer-assisted surgery is to replace marker-based tracking systems for instrument localization with pure image-based 6DoF pose estimation.
Incremental 3D Semantic Scene Graph Prediction from RGB Sequences
Our method consists of a novel incremental entity estimation pipeline and a scene graph prediction network.
SCOPE: Structural Continuity Preservation for Medical Image Segmentation
Although the preservation of shape continuity and physiological anatomy is a natural assumption in the segmentation of medical images, it is often neglected by deep learning methods that mostly aim for the statistical modeling of input data as pixels rather than interconnected structures.
DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation
Although purely transformer-based architectures showed promising performance in many computer vision tasks, many hybrid models consisting of CNN and transformer blocks are introduced to fit more specialized tasks.
SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis
To address this limitation, we propose a novel guidance approach for the sampling process in the diffusion model that leverages bounding box and segmentation map information at inference time without additional training data.
S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences
Statistical shape models (SSMs) are an established way to represent the anatomy of a population with various clinically relevant applications.
Whether and When does Endoscopy Domain Pretraining Make Sense?
In this work, we investigate the need for endoscopy domain-specific pretraining based on downstream objectives.
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks
Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data.
Prior-RadGraphFormer: A Prior-Knowledge-Enhanced Transformer for Generating Radiology Graphs from X-Rays
The extraction of structured clinical information from free-text radiology reports in the form of radiology graphs has been demonstrated to be a valuable approach for evaluating the clinical correctness of report-generation methods.
LABRAD-OR: Lightweight Memory Scene Graphs for Accurate Bimodal Reasoning in Dynamic Operating Rooms
The holistic representation of surgical scenes as semantic scene graphs (SGG), where entities are represented as nodes and relations between them as edges, is a promising direction for fine-grained semantic OR understanding.
Ranked #2 on
Scene Graph Generation
on 4D-OR
Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis
Automated diagnosis prediction from medical images is a valuable resource to support clinical decision-making.
MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization
We validate the generalizability of the proposed domain-independent segmentation approach on several datasets with varying parameters and machines.
Semantic Latent Space Regression of Diffusion Autoencoders for Vertebral Fracture Grading
We then regress the severity of the fracture as a function of the distance to this hyperplane, calibrating the results to the Genant scale.
Location-Free Scene Graph Generation
Although using significantly fewer labels during training, we achieve 74. 12\% of the location-supervised SOTA performance on Visual Genome and even outperform the best method on 4D-OR.
Ranked #1 on
Scene Graph Generation
on 4D-OR
Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs
Image synthesis driven by computer graphics achieved recently a remarkable realism, yet synthetic image data generated this way reveals a significant domain gap with respect to real-world data.
Pixel-Level Explanation of Multiple Instance Learning Models in Biomedical Single Cell Images
Explainability is a key requirement for computer-aided diagnosis systems in clinical decision-making.
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning
Querying the expert to annotate regions of interest in a WSI guides the formation of high-attention regions for MIL.
BEL: A Bag Embedding Loss for Transformer enhances Multiple Instance Whole Slide Image Classification
Recent MIL approaches produce highly informative bag level representations by utilizing the transformer architecture's ability to model the dependencies between instances.
IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction
Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems.
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning
However, the various types of breast tissue, such as glandular, fat, and lesions, differ in sound speed.
KST-Mixer: Kinematic Spatio-Temporal Data Mixer For Colon Shape Estimation
Kinematic data of a colonoscope and the colon, including positions and directions of their centerlines, are obtained using electromagnetic and depth sensors.
Lidar Upsampling with Sliced Wasserstein Distance
This in turn enables our method to employ a one-stage upsampling paradigm without the need for coarse and fine reconstruction.
Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging
In our work, we discuss direction-dependent changes in the scene and show that a physics-inspired rendering improves the fidelity of US image synthesis.
Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing
In this work, we propose a framework for autonomous robotic navigation for subretinal injection, based on intelligent real-time processing of iOCT volumes.
TexPose: Neural Texture Learning for Self-Supervised 6D Object Pose Estimation
In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images.
SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments
We introduce a zero-shot split for Tabletop Objects Dataset (TOD-Z) to enable this study and present a method that uses annotated objects to learn the ``objectness'' of pixels and generalize to unseen object categories in cluttered indoor environments.
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Pose Dataset with Household Objects in Realistic Scenarios
Estimating the 6D pose of objects is a major 3D computer vision problem.
DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation
Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or the edges in the graph.
Improved Techniques for the Conditional Generative Augmentation of Clinical Audio Data
In contrast to previously proposed fully convolutional models, the proposed model implements residual Squeeze and Excitation modules in the generator architecture.
What can we learn about a generated image corrupting its latent representation?
The purpose of this work is to investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck.
MonoGraspNet: 6-DoF Grasping with a Single RGB Image
6-DoF robotic grasping is a long-lasting but unsolved problem.
Segmenting Known Objects and Unseen Unknowns without Prior Knowledge
By doing so, for the first time in panoptic segmentation with unknown objects, our U3HS is trained without unknown categories, reducing assumptions and leaving the settings as unconstrained as in real-life scenarios.
Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion
Robotic ultrasound (US) imaging aims at overcoming some of the limitations of free-hand US examinations, e. g. difficulty in guaranteeing intra- and inter-operator repeatability.
Speckle2Speckle: Unsupervised Learning of Ultrasound Speckle Filtering Without Clean Data
To enable this, we make use of realistic ultrasound simulation techniques that allow for instantiation of several independent speckle realizations that represent the exact same tissue, thus allowing for the application of image reconstruction techniques that work with pairs of differently corrupted data.
DA$^2$ Dataset: Toward Dexterity-Aware Dual-Arm Grasping
In this paper, we introduce DA$^2$, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects.
CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning
The proposed hierarchical model achieves state-of-the-art shape classification in mean accuracy and yields results on par with the previous segmentation methods while requiring significantly fewer computations.
Spotlight on nerves: Portable multispectral optoacoustic imaging of peripheral nerve vascularization and morphology
Various morphological and functional parameters of peripheral nerves and their vascular supply are indicative of pathological changes due to injury or disease.
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning
By considering the consistency information with the diversity in the consistency-based embedding scheme, the proposed method could select more informative samples for labeling in the semi-supervised learning setting.
Unsupervised pre-training of graph transformers on patient population graphs
We find that our proposed pre-training methods help in modeling the data at a patient and population level and improve performance in different fine-tuning tasks on all datasets.
CACTUSS: Common Anatomical CT-US Space for US examinations
Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel.
CheXplaining in Style: Counterfactual Explanations for Chest X-rays using StyleGAN
Deep learning models used in medical image analysis are prone to raising reliability concerns due to their black-box nature.
Shape-Aware Masking for Inpainting in Medical Imaging
Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery.
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data
Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and devices, lead to distinctive data distributions.
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears.
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy
Light-sheet fluorescence microscopy (LSFM) is a cutting-edge volumetric imaging technique that allows for three-dimensional imaging of mesoscopic samples with decoupled illumination and detection paths.
U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images
To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classifies the dementia progression of the patient into cognitive normal (CN), cognitive impairment (MCI), and AD.
Virtual embeddings and self-consistency for self-supervised learning
We validate TriMix on eight benchmark datasets consisting of natural and medical images with an improvement of 2. 71% and 0. 41% better than the second-best models for both data types.
PRO-TIP: Phantom for RObust automatic ultrasound calibration by TIP detection
We propose a novel method to automatically calibrate tracked ultrasound probes.
BFS-Net: Weakly Supervised Cell Instance Segmentation from Bright-Field Microscopy Z-Stacks
Despite its broad availability, volumetric information acquisition from Bright-Field Microscopy (BFM) is inherently difficult due to the projective nature of the acquisition process.
PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects
Object pose estimation is crucial for robotic applications and augmented reality.
Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine
We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI.
VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation
The results demonstrate that proposed approach can effectively and accurately navigate the probe towards the longitudinal view of vessels.
Affective Medical Estimation and Decision Making via Visualized Learning and Deep Learning
With the advent of sophisticated machine learning (ML) techniques and the promising results they yield, especially in medical applications, where they have been investigated for different tasks to enhance the decision-making process.
SoftPool++: An Encoder-Decoder Network for Point Cloud Completion
We propose a novel convolutional operator for the task of point cloud completion.
Y-Net: A Spatiospectral Dual-Encoder Networkfor Medical Image Segmentation
Automated segmentation of retinal optical coherence tomography (OCT) images has become an important recent direction in machine learning for medical applications.
Ranked #1 on
Retinal OCT Layer Segmentation
on Duke SD-OCT
(using extra training data)
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models
One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced.
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation.
Learning Local Displacements for Point Cloud Completion
To this aim, we introduce a second model that assembles our layers within a transformer architecture.
Interpretable Vertebral Fracture Diagnosis
Do black-box neural network models learn clinically relevant features for fracture diagnosis?
FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays
The automation of chest X-ray reporting has garnered significant interest due to the time-consuming nature of the task.
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis
We show that training the agent against the prediction model can significantly improve the semantic features extracted for downstream classification tasks.
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions
We test our method on two medical datasets of patient records, TADPOLE and MIMIC-III, including imaging and non-imaging features and different prediction tasks.
Ranked #1 on
Length-of-Stay prediction
on MIMIC-III
Conditional Generative Data Augmentation for Clinical Audio Datasets
In this work, we propose a novel data augmentation method for clinical audio datasets based on a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP), operating on log-mel spectrograms.
4D-OR: Semantic Scene Graphs for OR Domain Modeling
Towards this goal, for the first time, we propose using semantic scene graphs (SSG) to describe and summarize the surgical scene.
Ranked #3 on
Scene Graph Generation
on 4D-OR
Longitudinal Self-Supervision for COVID-19 Pathology Quantification
For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans.
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Dense methods also improved pose estimation in the presence of occlusion.
Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study
Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis.
Know your sensORs -- A Modality Study For Surgical Action Classification
Existing datasets from OR room cameras are thus far limited in size or modalities acquired, leaving it unclear which sensor modalities are best suited for tasks such as recognizing surgical action from videos.
GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting
While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications.
Ranked #1 on
6D Pose Estimation
on LineMOD
(Mean ADD-S metric)
From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction
There have been numerous recently proposed methods for monocular depth prediction (MDP) coupled with the equally rapid evolution of benchmarking tools.
Do Explanations Explain? Model Knows Best
It is a mystery which input features contribute to a neural network's output.
Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport
Shape matching has been a long-studied problem for the computer graphics and vision community.
Transformers in Action: Weakly Supervised Action Segmentation
The video action segmentation task is regularly explored under weaker forms of supervision, such as transcript supervision, where a list of actions is easier to obtain than dense frame-wise labels.
A Variational Bayesian Method for Similarity Learning in Non-Rigid Image Registration
We propose a novel variational Bayesian formulation for diffeomorphic non-rigid registration of medical images, which learns in an unsupervised way a data-specific similarity metric.
3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection
Despite training only on a standard dataset, such as KITTI, augmenting with our vector fields significantly improves the generalization to differently shaped objects and scenes.
Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments
Indirect Time-of-Flight (I-ToF) imaging is a widespread way of depth estimation for mobile devices due to its small size and affordable price.
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration
We first present a small sequence of RGB-D images displaying a human-object interaction.
Object-aware Monocular Depth Prediction with Instance Convolutions
With the advent of deep learning, estimating depth from a single RGB image has recently received a lot of attention, being capable of empowering many different applications ranging from path planning for robotics to computational cinematography.
ColibriDoc: An Eye-in-Hand Autonomous Trocar Docking System
For this purpose, we present a platform for autonomous trocar docking that combines computer vision and a robotic setup.
MIGS: Meta Image Generation from Scene Graphs
We propose MIGS (Meta Image Generation from Scene Graphs), a meta-learning based approach for few-shot image generation from graphs that enables adapting the model to different scenes and increases the image quality by training on diverse sets of tasks.
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation
A novel temporal attention mechanism further processes the local geometric information in a global context across consecutive images.
Semantic Image Alignment for Vehicle Localization
Accurate and reliable localization is a fundamental requirement for autonomous vehicles to use map information in higher-level tasks such as navigation or planning.
CertainNet: Sampling-free Uncertainty Estimation for Object Detection
Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings.
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
We propose a method to identify features with predictive information in the input domain.
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Existing automatic and interactive segmentation models for medical images only use data from a single time point (static).
Adversarial Domain Feature Adaptation for Bronchoscopic Depth Estimation
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.
MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation
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.
Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks
Sickle cell disease (SCD) is a severe genetic hemoglobin disorder that results in premature destruction of red blood cells.
Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs
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
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.
Ranked #1 on
6D Pose Estimation using RGB
on Occlusion LineMOD
Unconditional Scene Graph Generation
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.
Tracked 3D Ultrasound and Deep Neural Network-based Thyroid Segmentation reduce Interobserver Variability in Thyroid Volumetry
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
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.
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
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.
Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP
Derived regions are consistent across different images and coincide with human-defined semantic classes on some datasets.
Deep Direct Volume Rendering: Learning Visual Feature Mappings From Exemplary Images
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.
Multimodal Semantic Scene Graphs for Holistic Modeling of Surgical Procedures
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.
Rethinking Ultrasound Augmentation: A Physics-Inspired Approach
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency.
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
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.
Towards Semantic Interpretation of Thoracic Disease and COVID-19 Diagnosis Models
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).
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features
Neural networks have demonstrated remarkable performance in classification and regression tasks on chest X-rays.
Neural Response Interpretation through the Lens of Critical Pathways
Is critical input information encoded in specific sparse pathways within the neural network?
IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction
The main novelty lies in the interpretable attention module (IAM), which directly operates on multi-modal features.
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks.
Ranked #1 on
3D Object Classification
on 3R-Scan
GLOWin: A Flow-based Invertible Generative Framework for Learning Disentangled Feature Representations in Medical Images
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.
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias
Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells.
Co-Generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data
The method leverages the availability of labelled data in a different domain.
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
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.
Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning
The framework uses the patient CT scan and the fractured vertebra label to build a virtual healthy spine using a high-level approach.
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.
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.
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.
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?
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
I Like to Move It: 6D Pose Estimation as an Action Decision Process
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