Search Results for author: Hongming Shan

Found 49 papers, 18 papers with code

Cross-head Supervision for Crowd Counting with Noisy Annotations

1 code implementation16 Mar 2023 Mingliang Dai, Zhizhong Huang, Jiaqi Gao, Hongming Shan, Junping Zhang

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.

Crowd Counting

Twin Contrastive Learning with Noisy Labels

no code implementations13 Mar 2023 Zhizhong Huang, Junping Zhang, Hongming Shan

In this paper, we present TCL, a novel twin contrastive learning model to learn robust representations and handle noisy labels for classification.

Contrastive Learning Learning with noisy labels

LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring

no code implementations21 Feb 2023 Zhihao Chen, Chuang Niu, Ge Wang, Hongming Shan

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.

Computed Tomography (CT) Deblurring +2

CORE: Learning Consistent Ordinal REpresentations for Image Ordinal Estimation

no code implementations15 Jan 2023 Yiming Lei, Zilong Li, Yangyang Li, Junping Zhang, Hongming Shan

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

1 code implementation14 Nov 2022 Jiaxin Ye, Xin-Cheng Wen, Yujie Wei, Yong Xu, KunHong Liu, Hongming Shan

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.

Speech Emotion Recognition

Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning

no code implementations21 Oct 2022 Jingqi Li, Jiaqi Gao, Yuzhen Zhang, Hongming Shan, Junping Zhang

Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer.

Gait Recognition

When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework and A New Benchmark

no code implementations17 Oct 2022 Zhizhong Huang, Junping Zhang, Hongming Shan

Extensive experimental results on five benchmark cross-age datasets demonstrate that MTLFace yields superior performance for both AIFR and FAS.

Age-Invariant Face Recognition Face Generation +1

FD-MAR: Fourier Dual-domain Network for CT Metal Artifact Reduction

no code implementations24 Jul 2022 Zilong Li, Qi Gao, Yaping Wu, Chuang Niu, Junping Zhang, Meiyun Wang, Ge Wang, Hongming Shan

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.

Computed Tomography (CT) Metal Artifact Reduction

SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization

1 code implementation9 May 2022 Weiyi Yu, Zhizhong Huang, Junping Zhang, Hongming Shan

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.

Data Augmentation Lesion Segmentation

Synergizing Physics/Model-based and Data-driven Methods for Low-Dose CT

no code implementations29 Mar 2022 Wenjun Xia, Hongming Shan, Ge Wang, Yi Zhang

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

no code implementations22 Mar 2022 Rodrigo de Barros Vimieiro, Chuang Niu, Hongming Shan, Lucas Rodrigues Borges, Ge Wang, Marcelo Andrade da Costa Vieira

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.

Medical Diagnosis

Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels

no code implementations15 Mar 2022 Yiming Lei, Haiping Zhu, Junping Zhang, Hongming Shan

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.

Image Classification Medical Image Classification +2

Learning Representation for Clustering via Prototype Scattering and Positive Sampling

1 code implementation23 Nov 2021 Zhizhong Huang, Jie Chen, Junping Zhang, Hongming Shan

The strengths of ProPos are avoidable class collision issue, uniform representations, well-separated clusters, and within-cluster compactness.

Contrastive Learning Deep Clustering +2

Impact of loss functions on the performance of a deep neural network designed to restore low-dose digital mammography

1 code implementation12 Nov 2021 Hongming Shan, Rodrigo de Barros Vimieiro, Lucas Rodrigues Borges, Marcelo Andrade da Costa Vieira, Ge Wang

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

1 code implementation24 Aug 2021 Zhizhong Huang, Junping Zhang, Yi Zhang, Hongming Shan

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

1 code implementation15 May 2021 Zhizhong Huang, Shouzhen Chen, Junping Zhang, Hongming Shan

Age progression and regression aim to synthesize photorealistic appearance of a given face image with aging and rejuvenation effects, respectively.

Knowledge Distillation regression

SelfGait: A Spatiotemporal Representation Learning Method for Self-supervised Gait Recognition

1 code implementation27 Mar 2021 Yiqun Liu, Yi Zeng, Jian Pu, Hongming Shan, Peiyang He, Junping Zhang

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.

Gait Recognition Representation Learning

MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising

no code implementations24 Mar 2021 Zexin Lu, Wenjun Xia, Yongqiang Huang, Hongming Shan, Hu Chen, Jiliu Zhou, Yi Zhang

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.

Computed Tomography (CT) Denoising +1

SPICE: Semantic Pseudo-labeling for Image Clustering

1 code implementation17 Mar 2021 Chuang Niu, Hongming Shan, Ge Wang

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.

Contrastive Learning Deep Clustering +4

RoutingGAN: Routing Age Progression and Regression with Disentangled Learning

no code implementations1 Feb 2021 Zhizhong Huang, Junping Zhang, Hongming Shan

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

no code implementations31 Jan 2021 Yiming Lei, Hongming Shan, Junping Zhang

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.

Classification General Classification +3

PFA-GAN: Progressive Face Aging with Generative Adversarial Network

2 code implementations7 Dec 2020 Zhizhong Huang, Shouzhen Chen, Junping Zhang, Hongming Shan

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.

Age Estimation

Meta Ordinal Regression Forest For Learning with Unsure Lung Nodules

no code implementations7 Dec 2020 Yiming Lei, Haiping Zhu, Junping Zhang, Hongming Shan

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.

Classification General Classification +3

Convolutional Ordinal Regression Forest for Image Ordinal Estimation

no code implementations7 Aug 2020 Haiping Zhu, Hongming Shan, Yuheng Zhang, Lingfu Che, Xiaoyang Xu, Junping Zhang, Jianbo Shi, Fei-Yue Wang

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.

Age Estimation regression

Stabilizing Deep Tomographic Reconstruction

no code implementations4 Aug 2020 Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shao-Yu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang

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.

Adversarial Attack Computed Tomography (CT) +1

Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction

no code implementations8 Jul 2020 Chuang Niu, Wenxiang Cong, Fenglei Fan, Hongming Shan, Mengzhou Li, Jimin Liang, Ge Wang

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.

Disentanglement Metal Artifact Reduction

Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network

no code implementations23 Jun 2020 Qing Lyu, Hongming Shan, Yibin Xie, Debiao Li, Ge Wang

As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort.

Computed Tomography (CT) Metal Artifact Reduction +2

Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction

1 code implementation9 Dec 2019 Huidong Xie, Hongming Shan, Wenxiang Cong, Chi Liu, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang

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.

Image Reconstruction

Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data

no code implementations13 Nov 2019 Huidong Xie, Hongming Shan, Ge Wang

Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture.

Computed Tomography (CT) Image Reconstruction

Look globally, age locally: Face aging with an attention mechanism

1 code implementation24 Oct 2019 Haiping Zhu, Zhizhong Huang, Hongming Shan, Junping Zhang

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

1 code implementation13 Oct 2019 Yu Gong, Hongming Shan, Yueyang Teng, Ning Tu, Ming Li, Guodong Liang, Ge Wang, Shan-Shan Wang

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.

Image Denoising Transfer Learning

Deep-learning-based Breast CT for Radiation Dose Reduction

no code implementations25 Sep 2019 Wenxiang Cong, Hongming Shan, Xiaohua Zhang, Shaohua Liu, Ruola Ning, Ge Wang

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.

Computed Tomography (CT) Image Reconstruction

Multi-Contrast Super-Resolution MRI Through a Progressive Network

no code implementations5 Aug 2019 Qing Lyu, Hongming Shan, Ge Wang

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.

Computed Tomography (CT) Image Super-Resolution

Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach

no code implementations23 Jul 2019 Hongming Shan, Christopher Wiedeman, Ge Wang, Yang Yang

Photoacoustic tomography seeks to reconstruct an acoustic initial pressure distribution from the measurement of the ultrasound waveforms.

Precipitation Nowcasting with Star-Bridge Networks

no code implementations18 Jul 2019 Yuan Cao, Qiuying Li, Hongming Shan, Zhizhong Huang, Lei Chen, Leiming Ma, Junping Zhang

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.

Video Prediction

MRI Super-Resolution with Ensemble Learning and Complementary Priors

no code implementations6 Jul 2019 Qing Lyu, Hongming Shan, Ge Wang

Finally, a convolutional neural network is used for ensemble learning that synergizes the outputs of GANs into the final MR super-resolution images.

Ensemble Learning Image Super-Resolution

Ordinal Distribution Regression for Gait-based Age Estimation

no code implementations27 May 2019 Haiping Zhu, Yuheng Zhang, Guohao Li, Junping Zhang, Hongming Shan

This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation.

Age Estimation regression

Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising

1 code implementation17 Jan 2019 Fenglei Fan, Hongming Shan, Mannudeep K. Kalra, Ramandeep Singh, Guhan Qian, Matthew Getzin, Yueyang Teng, Juergen Hahn, Ge Wang

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.

Image Denoising

Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?

1 code implementation8 Nov 2018 Hongming Shan, Atul Padole, Fatemeh Homayounieh, Uwe Kruger, Ruhani Doda Khera, Chayanin Nitiwarangkul, Mannudeep K. Kalra, Ge Wang

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.

Denoising Image Reconstruction

Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping

no code implementations30 Oct 2018 Yiming Lei, Yukun Tian, Hongming Shan, Junping Zhang, Ge Wang, Mannudeep Kalra

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.

Data Augmentation General Classification +3

Super-resolution MRI through Deep Learning

no code implementations16 Oct 2018 Qing Lyu, Chenyu You, Hongming Shan, Ge Wang

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)

no code implementations10 Aug 2018 Chenyu You, Guang Li, Yi Zhang, Xiaoliu Zhang, Hongming Shan, Shenghong Ju, Zhen Zhao, Zhuiyang Zhang, Wenxiang Cong, Michael W. Vannier, Punam K. Saha, Ge Wang

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.

Computed Tomography (CT) Image Restoration +1

Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

no code implementations2 May 2018 Chenyu You, Qingsong Yang, Hongming Shan, Lars Gjesteby, Guang Li, Shenghong Ju, Zhuiyang Zhang, Zhen Zhao, Yi Zhang, Wenxiang Cong, Ge Wang

However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality.

Computed Tomography (CT) Denoising

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

no code implementations15 Feb 2018 Hongming Shan, Yi Zhang, Qingsong Yang, Uwe Kruger, Mannudeep K. Kalra, Ling Sun, Wenxiang Cong, Ge Wang

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.

Computed Tomography (CT) Denoising +1

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