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ColorNet -- Estimating Colorfulness in Natural ImagesAug 22 2019Measuring the colorfulness of a natural or virtual scene is critical for many applications in image processing field ranging from capturing to display. In this paper, we propose the first deep learning-based colorfulness estimation metric. For this purpose, ... More
Noise Flow: Noise Modeling with Conditional Normalizing FlowsAug 22 2019Modeling and synthesizing image noise is an important aspect in many computer vision applications. The long-standing additive white Gaussian and heteroscedastic (signal-dependent) noise models widely used in the literature provide only a coarse approximation ... More
Sequential Rib Labeling and Segmentation in Chest X-Ray using Mask R-CNNAug 22 2019Mask R-CNN is a state-of-the-art network architecture for the detection and segmentation of object instances in the computer vision domain. In this contribution, it is used to localize, label and segment individual ribs in anterior-posterior chest X-ray ... More
Image Colorization By Capsule NetworksAug 22 2019In this paper, a simple topology of Capsule Network (CapsNet) is investigated for the problem of image colorization. The generative and segmentation capabilities of the original CapsNet topology, which is proposed for image classification problem, is ... More
Robustly segmenting quadriceps muscles of ultra-endurance athletes with weakly supervised U-NetAug 22 2019In this study, segmentation of quadriceps muscle heads of ultra-endurance athletes was done using a multi-atlas segmentation and corrective leaning framework where the registration based multi-atlas segmentation step was replaced with weakly supervised ... More
Contour Detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random FieldAug 22 2019In Cassini ISS (Imaging Science Subsystem) images, contour detection is often performed on disk-resolved object to accurately locate their center. Thus, the contour detection is a key problem. Traditional edge detection methods, such as Canny and Roberts, ... More
Motion correction of dynamic contrast enhanced MRI of the liverAug 22 2019Motion correction of dynamic contrast enhanced magnetic resonance images (DCE-MRI) is a challenging task, due to changes in image appearance. In this study a groupwise registration, using a principle component analysis (PCA) based metric,1 is evaluated ... More
Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentationAug 22 2019Most MRI liver segmentation methods use a structural 3D scan as input, such as a T1 or T2 weighted scan. Segmentation performance may be improved by utilizing both structural and functional information, as contained in dynamic contrast enhanced (DCE) ... More
Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste InspectionAug 22 2019Surface mount technology (SMT) is a process for producing printed circuit boards. Solder paste printer (SPP), package mounter, and solder reflow oven are used for SMT. The board on which the solder paste is deposited from the SPP is monitored by solder ... More
An Image Fusion Scheme for Single-Shot High Dynamic Range Imaging with Spatially Varying ExposuresAug 22 2019This paper proposes a novel multi-exposure image fusion (MEF) scheme for single-shot high dynamic range imaging with spatially varying exposures (SVE). Single-shot imaging with SVE enables us not only to produce images without color saturation regions ... More
Pro-Cam SSfM: Projector-Camera System for Structure and Spectral Reflectance from MotionAug 22 2019In this paper, we propose a novel projector-camera system for practical and low-cost acquisition of a dense object 3D model with the spectral reflectance property. In our system, we use a standard RGB camera and leverage an off-the-shelf projector as ... More
Building change detection based on multi-scale filtering and grid partitionAug 22 2019Building change detection is of great significance in high resolution remote sensing applications. Multi-index learning, one of the state-of-the-art building change detection methods, still has drawbacks like incapability to find change types directly ... More
Statistical characterization of scattering delay in synthetic aperture radar imagingAug 21 2019Distinguishing between the instantaneous and delayed scatterers in synthetic aperture radar (SAR) images is important for target identification and characterization. To perform this task, one can use the autocorrelation analysis of coordinate-delay images. ... More
DUAL-GLOW: Conditional Flow-Based Generative Model for Modality TransferAug 21 2019Positron emission tomography (PET) imaging is an imaging modality for diagnosing a number of neurological diseases. In contrast to Magnetic Resonance Imaging (MRI), PET is costly and involves injecting a radioactive substance into the patient. Motivated ... More
Boundary Aware Networks for Medical Image SegmentationAug 21 2019Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, ... More
Pixel-wise Segmentation of Right Ventricle of HeartAug 21 2019One of the first steps in the diagnosis of most cardiac diseases, such as pulmonary hypertension, coronary heart disease is the segmentation of ventricles from cardiac magnetic resonance (MRI) images. Manual segmentation of the right ventricle requires ... More
DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging AnalysisAug 21 2019Calcium imaging is one of the most important tools in neurophysiology as it enables the observation of neuronal activity for hundreds of cells in parallel and at single-cell resolution. In order to use the data gained with calcium imaging, it is necessary ... More
DISCo for the CIA: Deep learning, Instance Segmentation, and Correlations for Calcium Imaging AnalysisAug 21 2019Aug 22 2019Calcium imaging is one of the most important tools in neurophysiology as it enables the observation of neuronal activity for hundreds of cells in parallel and at single-cell resolution. In order to use the data gained with calcium imaging, it is necessary ... More
TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-raysAug 21 2019In this work, we exploit the unsupervised domain adaptation problem for radiology image interpretation across domains. Specifically, we study how to adapt the disease recognition model from a labeled source domain to an unlabeled target domain, so as ... More
Representation Disentanglement for Multi-task Learning with application to Fetal UltrasoundAug 21 2019One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the ... More
A CNN toolbox for skin cancer classificationAug 21 2019We describe a software toolbox for the configuration of deep neural networks in the domain of skin cancer classification. The implemented software architecture allows developers to quickly set up new convolutional neural network (CNN) architectures and ... More
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation LearningAug 21 2019The ability to synthesise Computed Tomography images - commonly known as pseudo CT, or pCT - from MRI input data is commonly assessed using an intensity-wise similarity, such as an L2-norm between the ground truth CT and the pCT. However, given that the ... More
U-Net Training with Instance-Layer NormalizationAug 21 2019Normalization layers are essential in a Deep Convolutional Neural Network (DCNN). Various normalization methods have been proposed. The statistics used to normalize the feature maps can be computed at batch, channel, or instance level. However, in most ... More
Dataset Growth in Medical Image Analysis ResearchAug 21 2019Medical image analysis studies usually require medical image datasets for training, testing and validation of algorithms. The need is underscored by the deep learning revolution and the dominance of machine learning in recent medical image analysis research. ... More
A Realistic Face-to-Face Conversation System based on Deep Neural NetworksAug 21 2019To improve the experiences of face-to-face conversation with avatar, this paper presents a novel conversation system. It is composed of two sequence-to-sequence models respectively for listening and speaking and a Generative Adversarial Network (GAN) ... More
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture AnalysisAug 21 2019The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with ... More
CNN-Based Segmentation of the Cardiac Chambers and Great Vessels in Non-Contrast-Enhanced Cardiac CTAug 21 2019Quantification of cardiac structures in non-contrast CT (NCCT) could improve cardiovascular risk stratification. However, setting a manual reference to train a fully convolutional network (FCN) for automatic segmentation of NCCT images is hardly feasible, ... More
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain AdaptationAug 21 2019Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) ... More
Lung segmentation on chest x-ray images in patients with severe abnormal findings using deep learningAug 21 2019Rationale and objectives: Several studies have evaluated the usefulness of deep learning for lung segmentation using chest x-ray (CXR) images with small- or medium-sized abnormal findings. Here, we built a database including both CXR images with severe ... More
Efficient Sensing of Correlated Spatiotemporal Signals: A Stochastic Gradient ApproachAug 21 2019A significantly low cost and tractable progressive learning approach is proposed and discussed for efficient spatiotemporal monitoring of a completely unknown, two dimensional correlated signal distribution in localized wireless sensor field. The spatial ... More
Joint Motion Estimation and Segmentation from Undersampled Cardiac MR ImageAug 20 2019Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. However, if the main purpose is to extract certain quantitative measures ... More
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentationAug 20 2019Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. ... More
Multi-Modal Recognition of Worker Activity for Human-Centered Intelligent ManufacturingAug 20 2019In a human-centered intelligent manufacturing system, sensing and understanding of the worker's activity are the primary tasks. In this paper, we propose a novel multi-modal approach for worker activity recognition by leveraging information from different ... More
A Novel method for IDC Prediction in Breast Cancer Histopathology images using Deep Residual Neural NetworksAug 20 2019Invasive ductal carcinoma (IDC), which is also sometimes known as the infiltrating ductal carcinoma, is the most regular form of breast cancer. It accounts for about 80% of all breast cancers. According to the American Cancer Society, more than 180,000 ... More
Unsupervised Multi-modal Style Transfer for Cardiac MR SegmentationAug 20 2019Aug 21 2019In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated ... More
Unsupervised Multi-modal Style Transfer for Cardiac MR SegmentationAug 20 2019In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated ... More
Ranking Viscous Finger Simulations to an Acquired Ground Truth with Topology-aware MatchingsAug 20 2019This application paper presents a novel framework based on topological data analysis for the automatic evaluation and ranking of viscous finger simulation runs in an ensemble with respect to a reference acquisition. Individual fingers in a given time-step ... More
Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic DataAug 20 2019Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room. Fast and accurate identification and localization of the endotracheal (ET) tube is critical for the patient. In this study we propose a novel automated ... More
Direct Neural Network 3D Image Reconstruction of Radon Encoded DataAug 19 2019Neural network image reconstruction directly from measurement data is a growing field of research, but until now has been limited to producing small (e.g. 128x128) 2D images by the large memory requirements of the previously suggested networks. In order ... More
Deep Active Lesion SegmentationAug 19 2019Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS), a ... More
A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral UnmixingAug 19 2019Introducing spatial prior information in hyperspectral imaging (HSI) analysis has led to an overall improvement of the performance of many HSI methods applied for denoising, classification, and unmixing. Extending such methodologies to nonlinear settings ... More
Models Genesis: Generic Autodidactic Models for 3D Medical Image AnalysisAug 19 2019Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (e.g., CT ... More
Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning PriorAug 19 2019Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise. Thus, generating ... More
Deep neural network or dermatologist?Aug 19 2019Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult ... More
Adversarial Defense by Suppressing High-frequency ComponentsAug 19 2019Recent works show that deep neural networks trained on image classification dataset bias towards textures. Those models are easily fooled by applying small high-frequency perturbations to the clean images. In this paper, we learn robust image classification ... More
A hue-preserving tone mapping scheme based on constant-hue plane without gamut problemAug 19 2019We propose a novel hue-preserving tone mapping scheme. Various tone mapping operations have been studied so far, but there are very few works on color distortion caused in image tone mapping. First, LDR images produced from HDR ones by using conventional ... More
Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial VehiclesAug 18 2019This paper describes preliminary work in the recent promising approach of generating synthetic training data for facilitating the learning procedure of deep learning (DL) models, with a focus on aerial photos produced by unmanned aerial vehicles (UAV). ... More
Image Formation Model Guided Deep Image Super-ResolutionAug 18 2019We present a simple and effective image super-resolution algorithm that imposes an image formation constraint on the deep neural networks via pixel substitution. The proposed algorithm first uses a deep neural network to estimate intermediate high-resolution ... More
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI studyAug 18 2019Objectives: To evaluate the performance of an Artificial Intelligence (AI) system (Pegasus, Visulytix Ltd., UK), at the detection of Diabetic Retinopathy (DR) from images captured by a handheld portable fundus camera. Methods: A cohort of 6,404 patients ... More
Geometric Disentanglement for Generative Latent Shape ModelsAug 18 2019Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun to be explored is the use of latent representations of generative models. ... More
Asynchronous Single-Photon 3D ImagingAug 18 2019Single-photon avalanche diodes (SPADs) are becoming popular in time-of-flight depth-ranging due to their unique ability to capture individual photons with picosecond timing resolution. However, ambient light (e.g., sunlight) incident on a SPAD-based 3D ... More
EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image SegmentationAug 17 2019Translation of fully automated deep learning based medical image segmentation technologies to clinical workflows face two main algorithmic challenges. The first, is the collection and archival of large quantities of manually annotated ground truth data ... More
Locally Linear Embedding and fMRI feature selection in psychiatric classificationAug 17 2019Aug 20 2019Background: Functional magnetic resonance imaging (fMRI) provides non-invasive measures of neuronal activity using an endogenous Blood Oxygenation-Level Dependent (BOLD) contrast. This article introduces a nonlinear dimensionality reduction (Locally Linear ... More
Locally Linear Embedding and fMRI feature selection in psychiatric classificationAug 17 2019Background: Functional magnetic resonance imaging (fMRI) provides non-invasive measures of neuronal activity using an endogenous Blood Oxygenation-Level Dependent (BOLD) contrast. This article introduces a nonlinear dimensionality reduction (Locally Linear ... More
U-CAM: Visual Explanation using Uncertainty based Class Activation MapsAug 17 2019Understanding and explaining deep learning models is an imperative task. Towards this, we propose a method that obtains gradient-based certainty estimates that also provide visual attention maps. Particularly, we solve for visual question answering task. ... More
No-Reference Light Field Image Quality Assessment Based on Spatial-Angular MeasurementAug 17 2019Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely ... More
Impacts of Retina-related Zones on Quality Perception of Omnidirectional ImageAug 17 2019Virtual Reality (VR), which brings immersive experiences to viewers, has been gaining popularity in recent years. A key feature in VR systems is the use of omnidirectional content, which provides 360-degree views of scenes. In this work, we study the ... More
Conv2Warp: An unsupervised deformable image registration with continuous convolution and warpingAug 16 2019Recent successes in deep learning based deformable image registration (DIR) methods have demonstrated that complex deformation can be learnt directly from data while reducing computation time when compared to traditional methods. However, the reliance ... More
Detecting abnormalities in resting-state dynamics: An unsupervised learning approachAug 16 2019Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain. While many rs-fMRI studies have focused on static measures of functional ... More
Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and LocalizationAug 16 2019Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we train a GAN to remove an object, if present, from an image while otherwise ... More
Adversarial point perturbations on 3D objectsAug 16 2019The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks, like autonomous driving. We examine this problem from the perspective of the attacker, which is necessary in understanding how ... More
Robust Principal Component Analysis for Background Estimation of Particle Image Velocimetry DataAug 16 2019Particle Image Velocimetry (PIV) data processing procedures are adversely affected by light reflections and backgrounds as well as defects in the models and sticky particles that occlude the inner walls of the boundaries. In this paper, a novel approach ... More
Near, far, wherever you are: simulations on the dose efficiency of holographic and ptychographic coherent imagingAug 16 2019Different studies in x-ray microscopy have arrived at conflicting conclusions about the dose efficiency of imaging modes involving the recording of intensity distributions in the near (Fresnel regime) or far (Fraunhofer regime) field downstream of a specimen. ... More
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial LearningAug 16 2019Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform ... More
Knowledge distillation for semi-supervised domain adaptationAug 16 2019In the absence of sufficient data variation (e.g., scanner and protocol variability) in annotated data, deep neural networks (DNNs) tend to overfit during training. As a result, their performance is significantly lower on data from unseen sources compared ... More
Empirical Bayesian Mixture Models for Medical Image TranslationAug 16 2019Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpretable generative modelling approach to medical image translation. By allowing ... More
Variational Multi-Task MRI Reconstruction: Joint Reconstruction, Registration and Super-ResolutionAug 16 2019Motion degradation is a central problem in Magnetic Resonance Imaging (MRI). This work addresses the problem of how to obtain higher quality, super-resolved motion-free, reconstructions from highly undersampled MRI data. In this work, we present for the ... More
Occlusion-shared and Feature-separated Network for Occlusion Relationship ReasoningAug 16 2019Occlusion relationship reasoning demands closed contour to express the object, and orientation of each contour pixel to describe the order relationship between objects. Current CNN-based methods neglect two critical issues of the task: (1) simultaneous ... More
Multi-step Cascaded Networks for Brain Tumor SegmentationAug 16 2019Automatic brain tumor segmentation method plays an extremely important role in the whole process of brain tumor diagnosis and treatment. In this paper, we propose a multi-step cascaded network which takes the hierarchical topology of the brain tumor substructures ... More
Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapyAug 16 2019We propose a novel domain specific loss, which is a differentiable loss function based on the dose volume histogram, and combine it with an adversarial loss for the training of deep neural networks to generate Pareto optimal dose distributions. The mean ... More
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D EchocardiographyAug 16 2019Aug 22 2019Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far ... More
Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D EchocardiographyAug 16 2019Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far ... More
daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devicesAug 16 2019It is always well believed that Binary Neural Networks (BNNs) could drastically accelerate the inference efficiency by replacing the arithmetic operations in float-valued Deep Neural Networks (DNNs) with bit-wise operations. Nevertheless, there has not ... More
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth EstimationAug 15 2019Inspired by the success of adversarial learning, we propose a new end-to-end unsupervised deep learning framework for monocular depth estimation consisting of two Generative Adversarial Networks (GAN), deeply coupled with a structured Conditional Random ... More
MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box ConstraintsAug 15 2019Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents ... More
Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound ImagingAug 15 2019Limitations on bandwidth and power consumption impose strict bounds on data rates of diagnostic imaging systems. Consequently, the design of suitable (i.e. task- and data-aware) compression and reconstruction techniques has attracted considerable attention ... More
DeepHuMS: Deep Human Motion Signature for 3D Skeletal SequencesAug 15 20193D Human Motion Indexing and Retrieval is an interesting problem due to the rise of several data-driven applications aimed at analyzing and/or re-utilizing 3D human skeletal data, such as data-driven animation, analysis of sports bio-mechanics, human ... More
DeepHuMS: Deep Human Motion Signature for 3D Skeletal SequencesAug 15 2019Aug 19 20193D Human Motion Indexing and Retrieval is an interesting problem due to the rise of several data-driven applications aimed at analyzing and/or re-utilizing 3D human skeletal data, such as data-driven animation, analysis of sports bio-mechanics, human ... More
Automated classification of plasma regions using 3D particle energy distributionAug 15 2019Even though automatic classification and interpretation would be highly desired features for the Magnetospheric Multiscale mission (MMS), the gold rush era in machine learning has yet to reach the science done with observations collected by MMS. We investigate ... More
Fast Sub-millimeter Diffusion MRI using gSlider-SMS and SNR-Enhancing Joint ReconstructionAug 15 2019We evaluate a new approach for achieving diffusion MRI data with high spatial resolution, large volume coverage, and fast acquisition speed. A recent method called gSlider-SMS enables whole-brain sub-millimeter diffusion MRI with high signal-to-noise ... More
A Multimodal Vision Sensor for Autonomous DrivingAug 15 2019This paper describes a multimodal vision sensor that integrates three types of cameras, including a stereo camera, a polarization camera and a panoramic camera. Each sensor provides a specific dimension of information: the stereo camera measures depth ... More
A deep learning model for segmentation of geographic atrophy to study its long-term natural historyAug 15 2019Purpose: To develop and validate a deep learning model for automatic segmentation of geographic atrophy (GA) in color fundus images (CFIs) and its application to study growth rate of GA. Participants: 409 CFIs of 238 eyes with GA from the Rotterdam Study ... More
Towards multi-sequence MR image recovery from undersampled k-space dataAug 15 2019Aug 16 2019Undersampled MR image recovery has been widely studied for accelerated MR acquisition. However, it has been mostly studied under a single sequence scenario, despite the fact that multi-sequence MR scan is common in practice. In this paper, we aim to optimize ... More
Towards multi-sequence MR image recovery from undersampled k-space dataAug 15 2019Undersampled MR image recovery has been widely studied for accelerated MR acquisition. However, it has been mostly studied under a single sequence scenario, despite the fact that multi-sequence MR scan is common in practice. In this paper, we aim to optimize ... More
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating ObjectAug 15 2019Aug 16 2019Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them accurately and quickly from the background. Though considerable progress has been made, there still exist challenges for rotating ... More
R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating ObjectAug 15 2019Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them accurately and quickly from the background. Though considerable progress has been made, there still exist challenges for rotating ... More
Deep Slice Interpolation via Marginal Super-Resolution, Fusion and RefinementAug 15 2019We propose a marginal super-resolution (MSR) approach based on 2D convolutional neural networks (CNNs) for interpolating an anisotropic brain magnetic resonance scan along the highly under-sampled direction, which is assumed to axial without loss of generality. ... More
Resolving challenges in deep learning-based analyses of histopathological images using explanation methodsAug 15 2019Deep learning has recently gained popularity in digital pathology due to its high prediction quality. However, the medical domain requires explanation and insight for a better understanding beyond standard quantitative performance evaluation. Recently, ... More
Cosmological N-body simulations: a challenge for scalable generative modelsAug 15 2019Deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders have been demonstrated to produce images of high visual quality. However, the existing hardware on which these models are trained severely limits the size ... More
Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation ProblemsAug 15 2019Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods are inapplicable for small datasets, which are very common in medical problems. ... More
Automated Rib Fracture Detection of Postmortem Computed Tomography Images Using Machine Learning TechniquesAug 15 2019Imaging techniques is widely used for medical diagnostics. This leads in some cases to a real bottleneck when there is a lack of medical practitioners and the images have to be manually processed. In such a situation there is a need to reduce the amount ... More
Bypass Enhancement RGB Stream Model for Pedestrian Action Recognition of Autonomous VehiclesAug 15 2019Pedestrian action recognition and intention prediction are one of the core issues in the field of autonomous driving. In this research field, action recognition is one of the key technologies. A large number of scholars have done a lot of work to improve ... More
Multimodal Volume-Aware Detection and Segmentation for Brain Metastases RadiosurgeryAug 15 2019Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very effective, SRS currently requires manually intensive delineation of tumors. ... More
Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT AngiographyAug 14 2019Detection of coronary artery stenosis in coronary CT angiography (CCTA) requires highly personalized surface meshes enclosing the coronary lumen. In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices ... More
Robust parametric modeling of Alzheimer's disease progressionAug 14 2019Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies robust modeling of Alzheimer's disease progression using parametric methods. The ... More
Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?Aug 14 2019Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of volume-, density-, and texture-based ... More
Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and TechniquesAug 14 2019Diabetic Foot Ulcers (DFU) detection using computerized methods is an emerging research area with the evolution of machine learning algorithms. However, existing research focuses on detecting and segmenting the ulcers. According to DFU medical classification ... More
Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findingsAug 14 2019Early diagnosis of sacroiliitis may lead to preventive treatment which can significantly improve the patient's quality of life in the long run. Oftentimes, a CT scan of the lower back or abdomen is acquired for suspected back pain. However, since the ... More
Joint phase reconstruction and magnitude segmentation from velocity-encoded MRI dataAug 14 2019Velocity-encoded MRI is an imaging technique used in different areas to assess flow motion. Some applications include medical imaging such as cardiovascular blood flow studies, and industrial settings in the areas of rheology, pipe flows, and reactor ... More
A Research Framework for Virtual Reality Neurosurgery Based on Open-Source ToolsAug 14 2019Fully immersive virtual reality (VR) has the potential to improve neurosurgical planning. For example, it may offer 3D visualizations of relevant anatomical structures with complex shapes, such as blood vessels and tumors. However, there is a lack of ... More