Search Results for author:
Found 49 papers, 18 papers with code
Cross-head Supervision for Crowd Counting with Noisy Annotations
To alleviate the negative impact of noisy annotations, we propose a novel crowd counting model with one convolution head and one transformer head, in which these two heads can supervise each other in noisy areas, called Cross-Head Supervision.
Twin Contrastive Learning with Noisy Labels
In this paper, we present TCL, a novel twin contrastive learning model to learn robust representations and handle noisy labels for classification.
Ranked #18 on
Image Classification
on mini WebVision 1.0
LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring
Here, we propose to link in-plane and through-plane transformers for simultaneous in-plane denoising and through-plane deblurring, termed as LIT-Former, which can efficiently synergize in-plane and through-plane sub-tasks for 3D CT imaging and enjoy the advantages of both convolution and transformer networks.
CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation
However, the manifold of the resultant feature representations does not maintain the intrinsic ordinal relations of interest, which hinders the effectiveness of the image ordinal estimation.
Temporal Modeling Matters: A Novel Temporal Emotional Modeling Approach for Speech Emotion Recognition
Specifically, TIM-Net first employs temporal-aware blocks to learn temporal affective representation, then integrates complementary information from the past and the future to enrich contextual representations, and finally, fuses multiple time scale features for better adaptation to the emotional variation.
Ranked #1 on
Speech Emotion Recognition
on EMOVO
Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning
Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer.
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark
Extensive experimental results on five benchmark cross-age datasets demonstrate that MTLFace yields superior performance for both AIFR and FAS.
FD-MAR: Fourier Dual-domain Network for CT Metal Artifact Reduction
The presence of high-density objects such as metal implants and dental fillings can introduce severely streak-like artifacts in computed tomography (CT) images, greatly limiting subsequent diagnosis.
SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization
To tackle this issue, we introduce a self-adaptive normalization network, termed SAN-Net, to achieve adaptive generalization on unseen sites for stroke lesion segmentation.
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting
Crowd counting has important applications in public safety and pandemic control.
Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT
Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging.
Convolutional Neural Network to Restore Low-Dose Digital Breast Tomosynthesis Projections in a Variance Stabilization Domain
To accurately control the network operation point, in terms of noise and blur of the restored image, we propose a loss function that minimizes the bias and matches residual noise between the input and the output.
Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels
To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relationship through the combination of convolutional neural network and differential forest in a meta-learning framework.
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans.
Learning Representation for Clustering via Prototype Scattering and Positive Sampling
The strengths of ProPos are avoidable class collision issue, uniform representations, well-separated clusters, and within-cluster compactness.
Ranked #1 on
Image Clustering
on Imagenet-dog-15
Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography
Results showed that the perceptual loss function (PL4) is able to achieve virtually the same noise levels of a full-dose acquisition, while resulting in smaller signal bias compared to other loss functions.
DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising
To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains.
AgeFlow: Conditional Age Progression and Regression with Normalizing Flows
Age progression and regression aim to synthesize photorealistic appearance of a given face image with aging and rejuvenation effects, respectively.
SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition
In this work, we propose a self-supervised gait recognition method, termed SelfGait, which takes advantage of the massive, diverse, unlabeled gait data as a pre-training process to improve the representation abilities of spatiotemporal backbones.
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
Recent advance on neural network architecture search (NAS) has proved that the network architecture has a dramatic effect on the model performance, which indicates that current network architectures for LDCT may be sub-optimal.
SPICE: Semantic Pseudo-labeling for Image Clustering
In this paper, we present a Semantic Pseudo-labeling-based Image ClustEring (SPICE) framework, which divides the clustering network into a feature model for measuring the instance-level similarity and a clustering head for identifying the cluster-level discrepancy.
Ranked #1 on
Image Clustering
on CIFAR-100
When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework
We further validate MTLFace on two popular general face recognition datasets, showing competitive performance for face recognition in the wild.
Ranked #1 on
Age-Invariant Face Recognition
on FG-NET
RoutingGAN: Routing Age Progression and Regression with Disentangled Learning
Although impressive results have been achieved for age progression and regression, there remain two major issues in generative adversarial networks (GANs)-based methods: 1) conditional GANs (cGANs)-based methods can learn various effects between any two age groups in a single model, but are insufficient to characterize some specific patterns due to completely shared convolutions filters; and 2) GANs-based methods can, by utilizing several models to learn effects independently, learn some specific patterns, however, they are cumbersome and require age label in advance.
Meta ordinal weighting net for improving lung nodule classification
In this paper, we propose a Meta Ordinal Weighting Network (MOW-Net) to explicitly align each training sample with a meta ordinal set (MOS) containing a few samples from all classes.
PFA-GAN: Progressive Face Aging with Generative Adversarial Network
Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically use a single network to learn various aging effects between any two different age groups.
Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules
Recently, an unsure data model (UDM) was proposed to incorporate those unsure nodules by formulating this problem as an ordinal regression, showing better performance over traditional binary classification.
Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population.
Convolutional Ordinal Regression Forest for Image Ordinal Estimation
We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships.
Stabilizing Deep Tomographic Reconstruction
ACID synergizes a deep reconstruction network trained on big data, kernel awareness from CS-inspired processing, and iterative refinement to minimize the data residual relative to real measurement.
Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction
Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for training.
Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network
As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort.
Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction
Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences.
Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data
Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture.
Look globally, age locally: Face aging with an attention mechanism
Face aging is of great importance for cross-age recognition and entertainment-related applications.
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising
The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise low-dose PET images without compromising structural details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN.
Deep-learning-based Breast CT for Radiation Dose Reduction
In this study, we propose a deep-learning-based method to establish a residual neural network model for the image reconstruction, which is applied for few-view breast CT to produce high quality breast CT images.
Multi-Contrast Super-Resolution MRI Through a Progressive Network
Our experimental results demonstrate that the proposed networks can produce MRI super-resolution images with good image quality and outperform other multi-contrast super-resolution methods in terms of structural similarity and peak signal-to-noise ratio.
Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach
Photoacoustic tomography seeks to reconstruct an acoustic initial pressure distribution from the measurement of the ultrasound waveforms.
Precipitation Nowcasting with Star-Bridge Networks
Precipitation nowcasting, which aims to precisely predict the short-term rainfall intensity of a local region, is gaining increasing attention in the artificial intelligence community.
MRI Super-Resolution with Ensemble Learning and Complementary Priors
Finally, a convolutional neural network is used for ensemble learning that synergizes the outputs of GANs into the final MR super-resolution images.
Dual Network Architecture for Few-view CT -- Trained on ImageNet Data and Transferred for Medical Imaging
Few-view CT image reconstruction is an important topic to reduce the radiation dose.
Ordinal Distribution Regression for Gait-based Age Estimation
This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation.
Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising
Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic operation on input data, thereby enhancing the capability of an individual neuron.
Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
Here we design a novel neural network architecture for low-dose CT (LDCT) and compare it with commercial iterative reconstruction methods used for standard of care CT.
Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping
Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN.
Super-resolution MRI through Deep Learning
Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics.
Medical Physics
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network
Based on the transfer learning from 2D to 3D, the 3D network converges faster and achieves a better denoising performance than that trained from scratch.
