research
∙
08/23/2023
Development and external validation of a lung cancer risk estimation tool using gradient-boosting
Lung cancer is a significant cause of mortality worldwide, emphasizing t...
share
research
∙
07/21/2023
Consistency-guided Meta-Learning for Bootstrapping Semi-Supervised Medical Image Segmentation
Medical imaging has witnessed remarkable progress but usually requires a...
research
∙
04/26/2023
Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining
Medical artificial general intelligence (MAGI) enables one foundation mo...
research
∙
06/06/2022
Learning Treatment Plan Representations for Content Based Image Retrieval
Objective: Knowledge based planning (KBP) typically involves training an...
research
∙
03/15/2022
CSN: Component-Supervised Network for Few-Shot Classification
The few-shot classification (FSC) task has been a hot research topic in ...
research
∙
02/09/2022
Learning to Bootstrap for Combating Label Noise
Deep neural networks are powerful tools for representation learning, but...
research
∙
01/29/2022
Image Classification using Graph Neural Network and Multiscale Wavelet Superpixels
Prior studies using graph neural networks (GNNs) for image classificatio...
research
∙
12/03/2021
SSDL: Self-Supervised Dictionary Learning
The label-embedded dictionary learning (DL) algorithms generate influent...
research
∙
12/01/2021
MDFM: Multi-Decision Fusing Model for Few-Shot Learning
In recent years, researchers pay growing attention to the few-shot learn...
research
∙
11/23/2021
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Despite the routine use of electronic health record (EHR) data by radiol...
research
∙
09/28/2021
Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning
The presence of metallic implants often introduces severe metal artifact...
research
∙
09/16/2021
MHFC: Multi-Head Feature Collaboration for Few-Shot Learning
Few-shot learning (FSL) aims to address the data-scarce problem. A stand...
research
∙
09/07/2021
CIM: Class-Irrelevant Mapping for Few-Shot Classification
Few-shot classification (FSC) is one of the most concerned hot issues in...
research
∙
08/24/2021
NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction
Image reconstruction is an inverse problem that solves for a computation...
research
∙
05/25/2021
A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction
Deep learning affords enormous opportunities to augment the armamentariu...
research
∙
05/04/2021
Operator Splitting for Adaptive Radiation Therapy with Nonlinear Health Dynamics
We present an optimization-based approach to radiation treatment plannin...
research
∙
03/29/2021
DualNorm-UNet: Incorporating Global and Local Statistics for Robust Medical Image Segmentation
Batch Normalization (BN) is one of the key components for accelerating n...
research
∙
10/20/2020
Region-specific Dictionary Learning-based Low-dose Thoracic CT Reconstruction
This paper presents a dictionary learning-based method with region-speci...
research
∙
09/16/2020
Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images
Computed tomography (CT) has been widely used for medical diagnosis, ass...
research
∙
07/21/2020
Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis
The automatic diagnosis of various retinal diseases from fundus images i...
research
∙
07/10/2020
Multi-Domain Image Completion for Random Missing Input Data
Multi-domain data are widely leveraged in vision applications taking adv...
research
∙
06/23/2020
A Mean-Field Theory for Learning the Schönberg Measure of Radial Basis Functions
We develop and analyze a projected particle Langevin optimization method...
research
∙
11/23/2019
Atlas Based Segmentations via Semi-Supervised Diffeomorphic Registrations
Purpose: Segmentation of organs-at-risk (OARs) is a bottleneck in curren...
research
∙
11/06/2019
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
In recent years, significant progress has been made in developing more a...
research
∙
10/31/2019
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
Segmentation of livers and liver tumors is one of the most important ste...
research
∙
09/25/2019
A Mean-Field Theory for Kernel Alignment with Random Features in Generative Adversarial Networks
We propose a novel supervised learning method to optimize the kernel in ...
research
∙
03/15/2019
On Sample Complexity of Projection-Free Primal-Dual Methods for Learning Mixture Policies in Markov Decision Processes
We study the problem of learning policy of an infinite-horizon, discount...
research
∙
02/27/2019
Multiple Kernel Learning from U-Statistics of Empirical Measures in the Feature Space
We propose a novel data-driven method to learn multiple kernels in kerne...
research
∙
10/11/2017
Quantized Minimum Error Entropy Criterion
Comparing with traditional learning criteria, such as mean square error ...
research
∙
05/31/2017
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Magnetic resonance image (MRI) reconstruction is a severely ill-posed li...
research
∙
03/23/2017
Robustness of Maximum Correntropy Estimation Against Large Outliers
The maximum correntropy criterion (MCC) has recently been successfully a...
research
∙
12/21/2016
Robust Learning with Kernel Mean p-Power Error Loss
Correntropy is a second order statistical measure in kernel space, which...
research
∙
08/01/2016
Kernel Risk-Sensitive Loss: Definition, Properties and Application to Robust Adaptive Filtering
Nonlinear similarity measures defined in kernel space, such as correntro...
research
∙
04/12/2015