Search Results for author:
Found 42 papers, 14 papers with code
VDPI: Video Deblurring with Pseudo-inverse Modeling
Notably, our experimental results demonstrate that such modifications can significantly improve the performance of deep learning models for video deblurring.
Brighteye: Glaucoma Screening with Color Fundus Photographs based on Vision Transformer
Optic disc detection improves the sensitivity at 95% specificity from 79. 20% to 85. 70% for glaucoma detection and the Hamming distance from 0. 2470 to 0. 1250 for glaucomatous feature classification.
Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised Dimensionality Reduction for Clustering Gravitational Wave Glitches
In response to this challenge, we introduce the Cross-Temporal Spectrogram Autoencoder (CTSAE), a pioneering unsupervised method for the dimensionality reduction and clustering of gravitational wave glitches.
Focused Active Learning for Histopathological Image Classification
The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value.
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models
To reduce these artifacts and enhance the perceptual quality of the results, in this paper, we propose a general method that can be effectively used in most GAN-based super-resolution (SR) models by introducing essential spatial information into the training process.
Explainable Transformer Prototypes for Medical Diagnoses
Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions.
Real-World Atmospheric Turbulence Correction via Domain Adaptation
Atmospheric turbulence, a common phenomenon in daily life, is primarily caused by the uneven heating of the Earth's surface.
A Physics based Machine Learning Model to characterize Room Temperature Semiconductor Detectors in 3D
Our novel approach is the first to characterize a full 3D charge transport model of the RTSD.
YOLO-Angio: An Algorithm for Coronary Anatomy Segmentation
Coronary angiography remains the gold standard for diagnosis of coronary artery disease, the most common cause of death worldwide.
Ranked #2 on
Coronary Artery Segmentation
on ARCADE
Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detection
Multiple Instance Learning (MIL) has been widely applied to medical imaging diagnosis, where bag labels are known and instance labels inside bags are unknown.
Atmospheric Turbulence Correction via Variational Deep Diffusion
We use the learned information to further condition the diffusion model.
Video-Specific Query-Key Attention Modeling for Weakly-Supervised Temporal Action Localization
To better learn these action category queries, we exploit not only the features of the current input video but also the correlation between different videos through a novel video-specific action category query learner worked with a query similarity loss.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Automatic Camera Trajectory Control with Enhanced Immersion for Virtual Cinematography
User-generated cinematic creations are gaining popularity as our daily entertainment, yet it is a challenge to master cinematography for producing immersive contents.
DeepCOVID-Fuse: A Multi-modality Deep Learning Model Fusing Chest X-Radiographs and Clinical Variables to Predict COVID-19 Risk Levels
The pre-trained fusion model with only CXRs as input increases accuracy to 0. 632 and AUC to 0. 813 and with only clinical variables as input increases accuracy to 0. 539 and AUC to 0. 733.
A Deep Generative Approach to Oversampling in Ptychography
Traditional approaches with reduced overlap between scanning areas result in reconstructions with artifacts.
Compressive Ptychography using Deep Image and Generative Priors
However, reconstructions with less number of scan points lead to imaging artifacts and significant distortions, hindering a quantitative evaluation of the results.
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space
Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature.
Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations
We find that optimizing for classification only may lead to performance losses in regression, and vice versa.
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes
Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks.
Visual Explanations for Convolutional Neural Networks via Latent Traversal of Generative Adversarial Networks
Lack of explainability in artificial intelligence, specifically deep neural networks, remains a bottleneck for implementing models in practice.
Removing Blocking Artifacts in Video Streams Using Event Cameras
In this paper, we propose EveRestNet, a convolutional neural network designed to remove blocking artifacts in videostreams using events from neuromorphic sensors.
Snapshot Compressive Imaging: Principle, Implementation, Theory, Algorithms and Applications
Via novel optical designs, the 2D detector samples the HD data in a {\em compressive} manner; following this, algorithms are employed to reconstruct the desired HD data-cube.
SkinScan: Low-Cost 3D-Scanning for Dermatologic Diagnosis and Documentation
The utilization of computational photography becomes increasingly essential in the medical field.
E3D: Event-Based 3D Shape Reconstruction
We first introduce an event-to-silhouette (E2S) neural network module to transform a stack of event frames to the corresponding silhouettes, with additional neural branches for camera pose regression.
2-Step Sparse-View CT Reconstruction with a Domain-Specific Perceptual Network
Computed tomography is widely used to examine internal structures in a non-destructive manner.
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
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.
Examining the Benefits of Capsule Neural Networks
Capsule networks are a recently developed class of neural networks that potentially address some of the deficiencies with traditional convolutional neural networks.
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
We apply our method on the problem of fine-tuning for unseen image formation models and on removal of artifacts introduced by GANs.
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Probabilistic methods, such as Gaussian Processes (GP), have proven successful in modeling this setting.
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Results: The proposed algorithm is validated on the 2018 PhysioNet challenge dataset.
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions.
Adaptive Image Sampling using Deep Learning and its Application on X-Ray Fluorescence Image Reconstruction
We propose an XRF image inpainting approach to address the issue of long scanning time, thus speeding up the scanning process while still maintaining the possibility to reconstruct a high quality XRF image.
Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks
Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease.
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
Finally, we show that our proposed model, the VSRResFeatGAN model, outperforms current state-of-the-art SR models, both quantitatively and qualitatively.
DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks.
Efficient Video Object Segmentation via Network Modulation
Video object segmentation targets at segmenting a specific object throughout a video sequence, given only an annotated first frame.
Ranked #1 on
One-shot visual object segmentation
on YouTube-VOS 2018
(Jaccard (Seen) metric)
Deep Multi-view Models for Glitch Classification
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO.
Compressive Holographic Video
Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image.
DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing
In this paper, we propose a novel encoder-decoder neural network model referred to as DeepBinaryMask for video compressive sensing.
Robust and Low-Rank Representation for Fast Face Identification with Occlusions
In this paper we propose an iterative method to address the face identification problem with block occlusions.
Deep Fully-Connected Networks for Video Compressive Sensing
In this work we present a deep learning framework for video compressive sensing.
Sparse Bayesian Methods for Low-Rank Matrix Estimation
Recovery of low-rank matrices has recently seen significant activity in many areas of science and engineering, motivated by recent theoretical results for exact reconstruction guarantees and interesting practical applications.
