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Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image SynthesisJun 13 2019Skin lesion segmentation is a vital task in skin cancer diagnosis and further treatment. Although deep learning based approaches have significantly improved the segmentation accuracy, these algorithms are still reliant on having a large enough dataset ... More
The Replica Dataset: A Digital Replica of Indoor SpacesJun 13 2019We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-dynamic-range (HDR) textures, per-primitive semantic class and instance ... More
Unsupervised Image Noise Modeling with Self-Consistent GANJun 13 2019Noise modeling lies in the heart of many image processing tasks. However, existing deep learning methods for noise modeling generally require clean and noisy image pairs for model training; these image pairs are difficult to obtain in many realistic scenarios. ... More
Deep Variational Networks with Exponential Weighting for Learning Computed TomographyJun 13 2019Tomographic image reconstruction is relevant for many medical imaging modalities including X-ray, ultrasound (US) computed tomography (CT) and photoacoustics, for which the access to full angular range tomographic projections might be not available in ... More
S3: A Spectral-Spatial Structure Loss for Pan-Sharpening NetworksJun 13 2019Recently, many deep-learning-based pan-sharpening methods have been proposed for generating high-quality pan-sharpened (PS) satellite images. These methods focused on various types of convolutional neural network (CNN) structures, which were trained by ... More
Robust and interpretable blind image denoising via bias-free convolutional neural networksJun 13 2019Deep convolutional networks often append additive constant ("bias") terms to their convolution operations, enabling a richer repertoire of functional mappings. Biases are also used to facilitate training, by subtracting mean response over batches of training ... More
The Herbarium Challenge 2019 DatasetJun 12 2019Herbarium sheets are invaluable for botanical research, and considerable time and effort is spent by experts to label and identify specimens on them. In view of recent advances in computer vision and deep learning, developing an automated approach to ... More
GANPOP: Generative Adversarial Network Prediction of Optical Properties from Single Snapshot Wide-field ImagesJun 12 2019We present a deep learning framework for wide-field, content-aware estimation of absorption and scattering coefficients of tissues, called Generative Adversarial Network Prediction of Optical Properties (GANPOP). Spatial frequency domain imaging is used ... More
HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point CloudsJun 12 2019We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds. Inspired by Bilateral Convolutional Layers (BCL), we propose novel DownBCL, UpBCL, and CorrBCL operations that ... More
Visual Wake Words DatasetJun 12 2019The emergence of Internet of Things (IoT) applications requires intelligence on the edge. Microcontrollers provide a low-cost compute platform to deploy intelligent IoT applications using machine learning at scale, but have extremely limited on-chip memory ... More
Image-Adaptive GAN based ReconstructionJun 12 2019In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks. However, the representation capabilities of these methods ... More
Novel phase retrieval based on deep learning for fringe projection profilometry by only using one single fringeJun 12 2019Fringe projection profilometry (FPP) has become increasingly important for 3-D shape measurement because of its attributes of high-resolution, high-accuracy, and high-speed, etc. In the FPP, a phase retrieval process is necessary to retrieve the desired ... More
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-spaceJun 12 2019In fully sampled cardiac MR (CMR) acquisitions, motion can lead to corruption of k-space lines, which can result in artefacts in the reconstructed images. In this paper, we propose a method to automatically detect and correct motion-related artefacts ... More
Low Photon Budget Phase Retrieval with Perceptual Loss Trained Deep Neural NetworksJun 12 2019Deep neural networks (DNNs) are efficient solvers for ill-posed problems and have been shown to outperform classical optimization techniques in several computational imaging problems. DNNs are trained by solving an optimization problem implies the choice ... More
Modeling Generalized Rate-Distortion FunctionsJun 12 2019Many multimedia applications require precise understanding of the rate-distortion characteristics measured by the function relating visual quality to media attributes, for which we term it the generalized rate-distortion (GRD) function. In this study, ... More
Preparatory data analysis for the reconstruction of real-time MRI dataJun 12 2019Real-time magnetic resonance imaging (MRI) poses unique challenges related to the speed of data acquisition and to the degree of undersampling necessary to achieve this speed. This Master's thesis introduces and evaluates two pre-processing approaches ... More
Synthesizing Diverse Lung Nodules Wherever Massively: 3D Multi-Conditional GAN-based CT Image Augmentation for Object DetectionJun 12 2019Accurate computer-assisted diagnosis, relying on large-scale annotated pathological images, can alleviate the risk of overlooking the diagnosis. Unfortunately, in medical imaging, most available datasets are small/fragmented. To tackle this, as a Data ... More
Joint 3D Localization and Classification of Space Debris using a Multispectral Rotating Point Spread FunctionJun 11 2019We consider the problem of joint three-dimensional (3D) localization and material classification of unresolved space debris using a multispectral rotating point spread function (RPSF). The use of RPSF allows one to estimate the 3D locations of point sources ... More
Snapshot projection optical tomographyJun 11 2019We present a new plenoptic microscopy configuration consisting of an objective lens and a micro-lens array (MLA), which is used as a tube lens. The new system that we named as snapshot projection optical tomography (SPOT) can directly record the projection ... More
Automatic brain tissue segmentation in fetal MRI using convolutional neural networksJun 11 2019MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation ... More
Generative adversarial network for segmentation of motion affected neonatal brain MRIJun 11 2019Automatic neonatal brain tissue segmentation in preterm born infants is a prerequisite for evaluation of brain development. However, automatic segmentation is often hampered by motion artifacts caused by infant head movements during image acquisition. ... More
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image CompressionJun 11 2019Image compression is an essential approach for decreasing the size in bytes of the image without deteriorating the quality of it. Typically, classic algorithms are used but recently deep-learning has been successfully applied. In this work, is presented ... More
`Project & Excite' Modules for Segmentation of Volumetric Medical ScansJun 11 2019Jun 12 2019Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and variations thereof have been introduced to recalibrate feature maps channel- ... More
`Project & Excite' Modules for Segmentation of Volumetric Medical ScansJun 11 2019Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and variations thereof have been introduced to recalibrate feature maps channel- ... More
Semiparametric estimation for incoherent optical imagingJun 11 2019The theory of semiparametric estimation offers an elegant way to compute the Cram\'er-Rao bound for a parameter of interest in the midst of infinitely many nuisance parameters. Here I apply the theory to the problem of moment estimation for incoherent ... More
Evaluation of CT Image Synthesis Methods:From Atlas-based Registration to Deep LearningJun 11 2019Computed tomography (CT) is a widely used imaging modality for medical diagnosis and treatment. In electroencephalography (EEG), CT imaging is necessary for co-registering with magnetic resonance imaging (MRI) and for creating more accurate head models ... More
Compressed Sensing MRI via a Multi-scale Dilated Residual Convolution NetworkJun 11 2019Magnetic resonance imaging (MRI) reconstruction is an active inverse problem which can be addressed by conventional compressed sensing (CS) MRI algorithms that exploit the sparse nature of MRI in an iterative optimization-based manner. However, two main ... More
A Novel Cost Function for Despeckling using Convolutional Neural NetworksJun 11 2019Removing speckle noise from SAR images is still an open issue. It is well know that the interpretation of SAR images is very challenging and despeckling algorithms are necessary to improve the ability of extracting information. An urban environment makes ... More
Deep learning analysis of cardiac CT angiography for detection of coronary arteries with functionally significant stenosisJun 11 2019In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during ... More
PAN: Projective Adversarial Network for Medical Image SegmentationJun 11 2019Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D semantics in an effective yet computationally efficient way remains an open ... More
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolutionJun 11 2019This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network. Different from most existing works which rely on the utilization of the coil sensitivities ... More
Multiscale Nakagami parametric imaging for improved liver tumor localizationJun 11 2019Effective ultrasound tissue characterization is usually hindered by complex tissue structures. The interlacing of speckle patterns complicates the correct estimation of backscatter distribution parameters. Nakagami parametric imaging based on localized ... More
Transfer Learning for Ultrasound Tongue Contour Extraction with Different DomainsJun 10 2019Medical ultrasound technology is widely used in routine clinical applications such as disease diagnosis and treatment as well as other applications like real-time monitoring of human tongue shapes and motions as visual feedback in second language training. ... More
BowNet: Dilated Convolution Neural Network for Ultrasound Tongue Contour ExtractionJun 10 2019Ultrasound imaging is safe, relatively affordable, and capable of real-time performance. One application of this technology is to visualize and to characterize human tongue shape and motion during a real-time speech to study healthy or impaired speech ... More
Alzheimer's Disease Brain MRI Classification: Challenges and InsightsJun 10 2019In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural networks. However, we discover ... More
Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary ResultsJun 10 2019Radiomics is an exciting new area of texture research for extracting quantitative and morphological characteristics of pathological tissue. However, to date, only single images have been used for texture analysis. We have extended radiomic texture methods ... More
Superpixel Tensor Pooling for Visual Tracking using Multiple Midlevel Visual Cues FusionJun 10 2019In this paper, we propose a method called superpixel tensor pooling tracker which can fuse multiple midlevel cues captured by superpixels into sparse pooled tensor features. Our method first uses superpixel segmentation to produce different patches from ... More
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted LossJun 10 2019Automatic segmentation of vestibular schwannoma (VS) tumors from magnetic resonance imaging (MRI) would facilitate efficient and accurate volume measurement to guide patient management and improve clinical workflow. The accuracy and robustness is challenged ... More
Noninvasive super-resolution imaging through scattering mediaJun 10 2019Super-resolution imaging with advanced optical systems has been revolutionizing technical analysis in various fields from biological to physical sciences. However, many objects are hidden by strongly scattering media such as biological tissues or wall ... More
Bridge Deck Delamination Segmentation Based on Aerial Thermography Through Regularized Grayscale Morphological Reconstruction and Gradient StatisticsJun 09 2019Environmental and surface texture-induced temperature variation across the bridge deck is a major source of errors in delamination detection through thermography. This type of external noise poses a significant challenge for conventional quantitative ... More
Thermographic Laplacian-pyramid filtering to enhance delamination detection in concrete structureJun 09 2019Despite decades of efforts using thermography to detect delamination in concrete decks, challenges still exist in removing environmental noise from thermal images. The performance of conventional temperature-contrast approaches can be significantly limited ... More
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmJun 09 2019Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation ... More
Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural NetworksJun 09 2019Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have provided new insight into ... More
Super-resolution PET imaging using convolutional neural networksJun 09 2019Positron emission tomography (PET) suffers from severe resolution limitations which limit its quantitative accuracy. In this paper, we present a super-resolution (SR) imaging technique for PET based on convolutional neural networks (CNNs). To facilitate ... More
Consensus Neural Network for Medical Imaging Denoising with Only Noisy Training SamplesJun 09 2019Deep neural networks have been proved efficient for medical image denoising. Current training methods require both noisy and clean images. However, clean images cannot be acquired for many practical medical applications due to naturally noisy signal, ... More
Semi-supervised Complex-valued GAN for Polarimetric SAR Image ClassificationJun 09 2019Polarimetric synthetic aperture radar (PolSAR) images are widely used in disaster detection and military reconnaissance and so on. However, their interpretation faces some challenges, e.g., deficiency of labeled data, inadequate utilization of data information ... More
In Situ Cane Toad RecognitionJun 09 2019Cane toads are invasive, toxic to native predators, compete with native insectivores, and have a devastating impact on Australian ecosystems, prompting the Australian government to list toads as a key threatening process under the Environment Protection ... More
Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference AccuracyJun 08 2019Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference, achieving promising performance for object ... More
S-ConvNet: A Shallow Convolutional Neural Network Architecture for Neuromuscular Activity Recognition Using Instantaneous High-Density Surface EMG ImagesJun 08 2019The concept of neuromuscular activity recognition using instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the development of more fluid and natural muscle-computer interfaces. However, the existing approaches ... More
When Unseen Domain Generalization is Unnecessary? Rethinking Data AugmentationJun 07 2019Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, in clinically realistic environments, such methods have marginal performance due to differences in image domains, including different imaging protocols, ... More
Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging DataJun 07 2019The Importance Weighted Auto Encoder (IWAE) objective has been shown to improve the training of generative models over the standard Variational Auto Encoder (VAE) objective. Here, we derive importance weighted extensions to AVB and AAE. These latent variable ... More
A deep learning approach for automated detection of geographic atrophy from color fundus photographsJun 07 2019Purpose: To assess the utility of deep learning in the detection of geographic atrophy (GA) from color fundus photographs; secondary aim to explore potential utility in detecting central GA (CGA). Design: A deep learning model was developed to detect ... More
PHiSeg: Capturing Uncertainty in Medical Image SegmentationJun 07 2019Segmentation of anatomical structures and pathologies is inherently ambiguous. For instance, structure borders may not be clearly visible or different experts may have different styles of annotating. The majority of current state-of-the-art methods do ... More
Standardized spectral and radiometric calibration of consumer camerasJun 07 2019Consumer cameras, particularly onboard smartphones and UAVs, are now commonly used as scientific instruments. However, their data processing pipelines are not optimized for quantitative radiometry and their calibration is more complex than that of scientific ... More
DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural NetworksJun 07 2019Parcellation of whole-brain tractography streamlines is an important step for tract-based analysis of brain white matter microstructure. Existing fiber parcellation approaches rely on accurate registration between an atlas and the tractograms of an individual, ... More
Optimization of light fields in ghost imaging using dictionary learningJun 07 2019Ghost imaging (GI) is a novel imaging technique based on the second-order correlation of light fields. Due to limited number of samplings in practice, traditional GI methods often reconstructs objects with unsatisfactory quality. Although some reconstruction ... More
Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer AnalysisJun 07 2019Accurate and automatic analysis of breast MRI plays an important role in early diagnosis and successful treatment planning for breast cancer. Due to the heterogeneity nature, accurate diagnosis of tumors remains a challenging task. In this paper, we propose ... More
An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised PatientsJun 07 2019Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a ... More
An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised PatientsJun 07 2019Jun 12 2019Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a ... More
Selfie: Self-supervised Pretraining for Image EmbeddingJun 07 2019We introduce a pretraining technique called Selfie, which stands for SELF-supervised Image Embedding. Selfie generalizes the concept of masked language modeling to continuous data, such as images. Given masked-out patches in an input image, our method ... More
Decompose-and-Integrate Learning for Multi-class Segmentation in Medical ImagesJun 07 2019Segmentation maps of medical images annotated by medical experts contain rich spatial information. In this paper, we propose to decompose annotation maps to learn disentangled and richer feature transforms for segmentation problems in medical images. ... More
Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask TransferJun 07 2019Advanced driver assistance and automated driving systems rely on risk estimation modules to predict and avoid dangerous situations. Current methods use expensive sensor setups and complex processing pipeline, limiting their availability and robustness. ... More
Globally-Aware Multiple Instance Classifier for Breast Cancer ScreeningJun 07 2019Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and smaller ... More
V-NAS: Neural Architecture Search for Volumetric Medical Image SegmentationJun 06 2019Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation. However, 2D convolutions cannot fully leverage the rich spatial information ... More
Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional NetworkJun 06 2019In exploration seismology, seismic inversion refers to the process of inferring physical properties of the subsurface from seismic data. Knowledge of physical properties can prove helpful in identifying key structures in the subsurface for hydrocarbon ... More
Hierarchical Bayesian myocardial perfusion quantificationJun 06 2019Purpose: Tracer-kinetic models can be used for the quantitative assessment of contrast-enhanced MRI data. However, the model-fitting can produce unreliable results due to the limited data acquired and the high noise levels. Such problems are especially ... More
Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM ApproachJun 06 2019Rain removal has recently attracted increasing research attention, as it is able to enhance the visibility of rain videos. However, the existing learning based rain removal approaches for videos suffer from insufficient training data, especially when ... More
Occluded Face Recognition Using Low-rank Regression with Generalized Gradient DirectionJun 06 2019In this paper, a very effective method to solve the contiguous face occlusion recognition problem is proposed. It utilizes the robust image gradient direction features together with a variety of mapping functions and adopts a hierarchical sparse and low-rank ... More
Salient Building Outline Enhancement and Extraction Using Iterative L0 Smoothing and Line EnhancingJun 06 2019In this paper, our goal is salient building outline enhancement and extraction from images taken from consumer cameras using L0 smoothing. We address weak outlines and over-smoothing problem. Weak outlines are often undetected by edge extractors or easily ... More
Generative Model-Based Ischemic Stroke Lesion SegmentationJun 06 2019CT perfusion (CTP) has been used to triage ischemic stroke patients in the early stage, because of its speed, availability, and lack of contraindications. Perfusion parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit ... More
Anatomical Priors for Image Segmentation via Post-Processing with Denoising AutoencodersJun 05 2019Deep convolutional neural networks (CNN) proved to be highly accurate to perform anatomical segmentation of medical images. However, some of the most popular CNN architectures for image segmentation still rely on post-processing strategies (e.g. Conditional ... More
Improved low-count quantitative PET reconstruction with a variational neural networkJun 05 2019Image reconstruction in low-count PET is particularly challenging because gammas from natural radioactivity in Lu-based crystals cause high random fractions that lower the measurement signal-to-noise-ratio (SNR). In model-based image reconstruction (MBIR), ... More
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST LabelsJun 05 2019Accurate, automated lesion detection in Computed Tomography (CT) is an important yet challenging task due to the large variation of lesion types, sizes, locations and appearances. Recent work on CT lesion detection employs two-stage region proposal based ... More
Learning Shape Representation on Sparse Point Clouds for Volumetric Image SegmentationJun 05 2019Volumetric image segmentation with convolutional neural networks (CNNs) encounters several challenges, which are specific to medical images. Among these challenges are large volumes of interest, high class imbalances, and difficulties in learning shape ... More
Investigating the Lombard Effect Influence on End-to-End Audio-Visual Speech RecognitionJun 05 2019Several audio-visual speech recognition models have been recently proposed which aim to improve the robustness over audio-only models in the present of noise. However, almost all of them ignore the impact of the Lombard effect, i.e., the change in speaking ... More
Readout of fluorescence functional signals through highly scattering tissueJun 05 2019Fluorescence is a powerful mean to probe information processing in the mammalian brain. However, neuronal tissues are highly heterogeneous and thus opaque to light. A wide set of non-invasive or invasive techniques for scattered light rejection, optical ... More
OctopusNet: A Deep Learning Segmentation Network for Multi-modal Medical ImagesJun 05 2019Deep learning models, such as the fully convolutional network (FCN), have been widely used in 3D biomedical segmentation and achieved state-of-the-art performance. Multiple modalities are often used for disease diagnosis and quantification. Two approaches ... More
An Uncertainty-Driven GCN Refinement Strategy for Organ SegmentationJun 05 2019Organ segmentation is an important pre-processing step in many computer assisted intervention and computer assisted diagnosis methods. In recent years, CNNs have dominated the state of the art in this task. Organ segmentation scenarios present a challenging ... More
AI-Skin : Skin Disease Recognition based on Self-learning and Wide Data Collection through a Closed Loop FrameworkJun 05 2019There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of self-confidence, but ... More
A Robust Roll Angle Estimation Algorithm Based on Gradient DescentJun 05 2019This paper presents a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense disparity map by minimizing a global energy using golden section search algorithm. In ... More
Farm land weed detection with region-based deep convolutional neural networksJun 05 2019Machine learning has become a major field of research in order to handle more and more complex image detection problems. Among the existing state-of-the-art CNN models, in this paper a region-based, fully convolutional network, for fast and accurate object ... More
A semi-implicit relaxed Douglas-Rachford algorithm (sir-DR) for PtychograhpyJun 05 2019Alternating projection based methods, such as ePIE and rPIE, have been used widely in ptychography. However, they only work well if there are adequate measurements (diffraction patterns); in the case of sparse data (i.e. fewer measurements) alternating ... More
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI SegmentationJun 05 2019Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a global ... More
Infant Contact-less Non-Nutritive Sucking Pattern Quantification via Facial Gesture AnalysisJun 05 2019Non-nutritive sucking (NNS) is defined as the sucking action that occurs when a finger, pacifier, or other object is placed in the baby's mouth, but there is no nutrient delivered. In addition to providing a sense of safety, NNS even can be regarded as ... More
Artifact Disentanglement Network for Unsupervised Metal Artifact ReductionJun 05 2019Jun 06 2019Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods which rely heavily on synthesized data for training. However, as synthesized data may not perfectly simulate the underlying ... More
Artifact Disentanglement Network for Unsupervised Metal Artifact ReductionJun 05 2019Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods which rely heavily on synthesized data for training. However, as synthesized data may not perfectly simulate the underlying ... More
One-pass Multi-task Networks with Cross-task Guided Attention for Brain Tumor SegmentationJun 05 2019Class imbalance has been one of the major challenges for medical image segmentation. The model cascade (MC) strategy significantly alleviates class imbalance issue. In spite of its outstanding performance, this method leads to an undesired system complexity ... More
Accurate phase retrieval of complex point spread functions with deep residual neural networksJun 04 2019Phase retrieval, i.e. the reconstruction of phase information from intensity information, is a central problem in many optical systems. Here, we demonstrate that a deep residual neural net is able to quickly and accurately perform this task for arbitrary ... More
Encoding Invariances in Deep Generative ModelsJun 04 2019Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions. However, in several applications, training samples obey invariances that are \textit{a priori} known; for example, ... More
A Nonlinear Acceleration Method for Iterative AlgorithmsJun 04 2019Iterative methods have led to better understanding and solving problems such as missing sampling, deconvolution, inverse systems, impulsive and Salt and Pepper noise removal problems. However, the challenges such as the speed of convergence and or the ... More
Grid-based Localization Stack for Inspection Drones towards Automation of Large Scale Warehouse SystemsJun 04 2019SLAM based techniques are often adopted for solving the navigation problem for the drones in GPS denied environment. Despite the widespread success of these approaches, they have not yet been fully exploited for automation in a warehouse system due to ... More
Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment StudyJun 04 2019Objectives: To evaluate the performance of a deep learning based Artificial Intelligence (AI) software for detection of glaucoma from stereoscopic optic disc photographs, and to compare this performance to the performance of a large cohort of ophthalmologists ... More
Learning Deep Image Priors for Blind Image DenoisingJun 04 2019Image denoising is the process of removing noise from noisy images, which is an image domain transferring task, i.e., from a single or several noise level domains to a photo-realistic domain. In this paper, we propose an effective image denoising method ... More
Content Adaptive Optimization for Neural Image CompressionJun 04 2019Jun 05 2019The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches. While, so far, research has mainly focused on architecture and model improvements, ... More
Content Adaptive Optimization for Neural Image CompressionJun 04 2019The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches. While, so far, research has mainly focused on architecture and model improvements, ... More
Depth-Preserving Real-Time Arbitrary Style TransferJun 03 2019Style transfer is the process of rendering one image with some content in the style of another image, representing the style. Recent studies of Liu et al. (2017) have shown significant improvement of style transfer rendering quality by adjusting traditional ... More
A Curated Image Parameter Dataset from Solar Dynamics Observatory MissionJun 03 2019We provide a large image parameter dataset extracted from the Solar Dynamics Observatory (SDO) mission's AIA instrument, for the period of January 2011 through the current date, with the cadence of six minutes, for nine wavelength channels. The volume ... More
eSLAM: An Energy-Efficient Accelerator for Real-Time ORB-SLAM on FPGA PlatformJun 03 2019Simultaneous Localization and Mapping (SLAM) is a critical task for autonomous navigation. However, due to the computational complexity of SLAM algorithms, it is very difficult to achieve real-time implementation on low-power platforms.We propose an energy ... More
Fashion Editing with Multi-scale Attention NormalizationJun 03 2019Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage ... More
Deep Feature Learning from a Hospital-Scale Chest X-ray Dataset with Application to TB Detection on a Small-Scale DatasetJun 03 2019The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks. The recent emergence of a large Chest X-ray dataset opened the possibility ... More