335 citations · 1.2k across the 81 of their papers we have counts for
18 papers · 1 filter
Efficient Lung Ultrasound Severity Scoring Using Dedicated Feature Extractor
Jiaqi Guo, Yunan Wu, Evangelos Kaimakamis +4
With the advent of the COVID-19 pandemic, ultrasound imaging has emerged as a promising technique for COVID-19 detection, due to its non-invasive nature, affordability, and portabi…
RN-SDEs: Limited-Angle CT Reconstruction with Residual Null-Space Diffusion Stochastic Differential Equations
Jiaqi Guo, Santiago Lopez-Tapia, Wing Shun Li +7
Computed tomography is a widely used imaging modality with applications ranging from medical imaging to material analysis. One major challenge arises from the lack of scanning info…
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
Xijun Wang, Santiago López-Tapia, Alice Lucas +3
Generative Adversarial Networks (GANs) have shown great performance on super-resolution problems since they can generate more visually realistic images and video frames. However, t…
YOLO-Angio: An Algorithm for Coronary Anatomy Segmentation
Tom Liu, Hui Lin, Aggelos K. Katsaggelos +1
Coronary angiography remains the gold standard for diagnosis of coronary artery disease, the most common cause of death worldwide. While this procedure is performed more than 2 mil…
StenUNet: Automatic Stenosis Detection from X-ray Coronary Angiography
Hui Lin, Tom Liu, Aggelos Katsaggelos +1
Coronary angiography continues to serve as the primary method for diagnosing coronary artery disease (CAD), which is the leading global cause of mortality. The severity of CAD is q…
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection
Yunan Wu, Francisco M. Castro-Macías, Pablo Morales-Álvarez +2
Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown. Traditional MIL assum…
DeepCOVID-Fuse: A Multi-modality Deep Learning Model Fusing Chest X-Radiographs and Clinical Variables to Predict COVID-19 Risk Levels
Yunan Wu, Amil Dravid, Ramsey Michael Wehbe +1
Propose: To present DeepCOVID-Fuse, a deep learning fusion model to predict risk levels in patients with confirmed coronavirus disease 2019 (COVID-19) and to evaluate the performan…
A Deep Generative Approach to Oversampling in Ptychography
Semih Barutcu, Aggelos K. Katsaggelos, Doğa Gürsoy
Ptychography is a well-studied phase imaging method that makes non-invasive imaging possible at a nanometer scale. It has developed into a mainstream technique with various applica…
Denoising Fast X-Ray Fluorescence Raster Scans of Paintings
Henry Chopp, Alicia McGeachy, Matthias Alfeld +3
Macro x-ray fluorescence (XRF) imaging of cultural heritage objects, while a popular non-invasive technique for providing elemental distribution maps, is a slow acquisition process…
A Joint Intensity-Neuromorphic Event Imaging System for Resource Constrained Devices
Srutarshi Banerjee, Henry H. Chopp, Jianping Zhang +3
We present a novel adaptive multi-modal intensity-event algorithm to optimize an overall objective of object tracking under bit rate constraints for a host-chip architecture. The c…
Improving Acquisition Speed of X-Ray Ptychography through Spatial Undersampling and Regularization
Prasan Shedligeri, Florian Schiffers, Semih Barutcu +3
X-ray ptychography is one of the versatile techniques for nanometer resolution imaging. The magnitude of the diffraction patterns is recorded on a detector and the phase of the dif…
Removing Blocking Artifacts in Video Streams Using Event Cameras
Henry H. Chopp, Srutarshi Banerjee, Oliver Cossairt +1
In this paper, we propose EveRestNet, a convolutional neural network designed to remove blocking artifacts in videostreams using events from neuromorphic sensors. We first degrade…
Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications
Xin Yuan, David J. Brady, Aggelos K. Katsaggelos
Capturing high-dimensional (HD) data is a long-term challenge in signal processing and related fields. Snapshot compressive imaging (SCI) uses a two-dimensional (2D) detector to ca…
An Adaptive Video Acquisition Scheme for Object Tracking and its Performance Optimization
Srutarshi Banerjee, Henry H. Chopp, Juan G. Serra +3
We present a novel adaptive host-chip modular architecture for video acquisition to optimize an overall objective task constrained under a given bit rate. The chip is a high resolu…
SkinScan: Low-Cost 3D-Scanning for Dermatologic Diagnosis and Documentation
Merlin A. Nau, Florian Schiffers, Yunhao Li +7
The utilization of computational photography becomes increasingly essential in the medical field. Today, imaging techniques for dermatology range from two-dimensional (2D) color im…
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network
Haoyu Wei, Florian Schiffers, Tobias Würfl +4
Computed tomography is widely used to examine internal structures in a non-destructive manner. To obtain high-quality reconstructions, one typically has to acquire a densely sample…
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
Emanuel A. Azcona, Pierre Besson, Yunan Wu +6
We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learn…
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
Alice Lucas, Santiago Lopez-Tapia, Rafael Molina +1
While Convolutional Neural Networks (CNNs) trained for image and video super-resolution (SR) regularly achieve new state-of-the-art performance, they also suffer from significant d…