
Simultaneous superresolution and motion artifact removal in diffusionweighted MRI using unsupervised deep learning
Diffusionweighted MRI is nowadays performed routinely due to its progno...
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Feature Disentanglement in generating threedimensional structure from twodimensional slice with sliceGAN
Deep generative models are known to be able to model arbitrary probabili...
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Axialtolateral superresolution for 3D fluorescence microscopy using unsupervised deep learning
Volumetric imaging by fluorescence microscopy is often limited by anisot...
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Cyclefree CycleGAN using Invertible Generator for Unsupervised LowDose CT Denoising
Recently, CycleGAN was shown to provide highperformance, ultrafast den...
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Vision Transformer using Lowlevel Chest Xray Feature Corpus for COVID19 Diagnosis and Severity Quantification
Developing a robust algorithm to diagnose and quantify the severity of C...
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Unifying domain adaptation and selfsupervised learning for CXR segmentation via AdaINbased knowledge distillation
As the segmentation labels are scarce, extensive researches have been co...
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PyNETCA: Enhanced PyNET with Channel Attention for EndtoEnd Mobile Image Signal Processing
Reconstructing RGB image from RAW data obtained with a mobile device is ...
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Diagonal Attention and Stylebased GAN for ContentStyle Disentanglement in Image Generation and Translation
One of the important research topics in image generative models is to di...
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Unsupervised Missing Cone Deep Learning in Optical Diffraction Tomography
Optical diffraction tomography (ODT) produces three dimensional distribu...
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Severity Quantification and Lesion Localization of COVID19 on CXR using Vision Transformer
Under the global pandemic of COVID19, building an automated framework t...
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Vision Transformer for COVID19 CXR Diagnosis using Chest Xray Feature Corpus
Under the global COVID19 crisis, developing robust diagnosis algorithm ...
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CycleQSM: Unsupervised QSM Deep Learning using PhysicsInformed CycleGAN
Quantitative susceptibility mapping (QSM) is a useful magnetic resonance...
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Continuous Conversion of CT Kernel using Switchable CycleGAN with AdaIN
In Xray computed tomography (CT) reconstruction, different filter kerne...
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DeepPhaseCut: Deep Relaxation in Phase for Unsupervised Fourier Phase Retrieval
Fourier phase retrieval is a classical problem of restoring a signal onl...
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Unsupervised MR Motion Artifact Deep Learning using OutlierRejecting Bootstrap Aggregation
Recently, deep learning approaches for MR motion artifact correction hav...
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AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the...
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Switchable Deep Beamformer
Recent proposals of deep beamformers using deep neural networks have att...
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Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGAN
Recently, deep learning approaches for accelerated MRI have been extensi...
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CycleMorph: Cycle Consistent Unsupervised Deformable Image Registration
Image registration is a fundamental task in medical image analysis. Rece...
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AdaINSwitchable CycleGAN for Efficient Unsupervised LowDose CT Denoising
Recently, deep learning approaches have been extensively studied for low...
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TwoStage Deep Learning for Accelerated 3D TimeofFlight MRA without Matched Training Data
Timeofflight magnetic resonance angiography (TOFMRA) is one of the mo...
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OTdriven MultiDomain Unsupervised Ultrasound Image Artifact Removal using a Single CNN
Ultrasound imaging (US) often suffers from distinct image artifacts from...
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Unsupervised CT Metal Artifact Learning using Attentionguided betaCycleGAN
Metal artifact reduction (MAR) is one of the most important research top...
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Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
Ultrasound (US) imaging is a fast and noninvasive imaging modality whic...
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Deep Learning COVID19 Features on CXR using Limited Training Data Sets
Under the global pandemic of COVID19, the use of artificial intelligenc...
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Unsupervised Deep Learning for MR Angiography with Flexible Temporal Resolution
Timeresolved MR angiography (tMRA) has been widely used for dynamic con...
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Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction
Recently, deep learning approaches have been extensively investigated to...
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Unsupervised Denoising for Satellite Imagery using Wavelet Subband CycleGAN
Multispectral satellite imaging sensors acquire various spectral band i...
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Understanding Graph Isomorphism Network for Brain MR Functional Connectivity Analysis
Graph neural networks (GNN) rely on graph operations that include neural...
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Structured LowRank Algorithms: Theory, MR Applications, and Links to Machine Learning
In this survey, we provide a detailed review of recent advances in the r...
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Optimal Transport, CycleGAN, and Penalized LS for Unsupervised Learning in Inverse Problems
The penalized least squares (PLS) is a classic approach to inverse probl...
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CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry
Deconvolution microscopy has been extensively used to improve the resolu...
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Adaptive and Compressive Beamforming using Deep Learning for Medical Ultrasound
In ultrasound (US) imaging, various types of adaptive beamforming techni...
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Unsupervised Deformable Image Registration Using CycleConsistent CNN
Medical image registration is one of the key processing steps for biomed...
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Boosting CNN beyond Label in Inverse Problems
Convolutional neural networks (CNN) have been extensively used for inver...
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Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal
Conebeam CT using a circular trajectory is quite often used for various ...
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Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN
Thanks to the recent success of generative adversarial network (GAN) for...
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Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer
Deconvolution microscopy has been extensively used to improve the resolu...
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Multiphase LevelSet Loss for SemiSupervised and Unsupervised Segmentation with Deep Learning
Recent stateoftheart image segmentation algorithms are mostly based o...
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Deep Learningbased Universal Beamformer for Ultrasound Imaging
In ultrasound (US) imaging, individual channel RF measurements are back...
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Image Reconstruction: From Sparsity to Dataadaptive Methods and Machine Learning
The field of image reconstruction has undergone four waves of methods. T...
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CollaGAN : Collaborative GAN for Missing Image Data Imputation
In many applications requiring multiple inputs to obtain a desired outpu...
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Understanding Geometry of EncoderDecoder CNNs
Encoderdecoder networks using convolutional neural network (CNN) archit...
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Universal Deep Beamformer for Variable Rate Ultrasound Imaging
Ultrasound (US) imaging is based on the timereversal principle, in whic...
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One Network to Solve All ROIs: Deep Learning CT for Any ROI using Differentiated Backprojection
Computed tomography for regionofinterest (ROI) reconstruction has adva...
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Direct Reconstruction of Saturated Samples in BandLimited OFDM Signals
Given a set of samples, a few of them being possibly saturated, we propo...
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Cycle Consistent Adversarial Denoising Network for Multiphase Coronary CT Angiography
In coronary CT angiography, a series of CT images are taken at different...
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kSpace Deep Learning for Parallel MRI: Application to TimeResolved MR Angiography
Timeresolved angiography with interleaved stochastic trajectories (TWIS...
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kSpace Deep Learning for Referencefree EPI Ghost Correction
Nyquist ghost artifacts in EPI images are originated from phase mismatch...
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kSpace Deep Learning for Accelerated MRI
The annihilating filterbased lowrank Hanel matrix approach (ALOHA) is ...
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Jong Chul Ye
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KAIST Endowed Chair Professor, Professor, Department of Bio and Brain Engineering in Department of Mathematical Sciences at Korea Advanced Institute of Science & Technology (KAIST), Associate and Assistant Professor at KAIST, Adjunct Professor, Department of Electrical Engineering, KAIST, Interim Department Head, Dept. of Bio and Brain Engineering at KAIST from 20142015, Senior Researcher, Xray CT Technology Group, GE Global Research Center, Niskayuna, New York from 20032004, Senior Member Research Staff, Philips Research Center, Briarcliff Manor, New York from 20012003, Postdoctoral Research, Dept. of Electrical and Computer Engineering, Univ. of Illinois at UrbanaChampaign from 19992001