Search Results for author: Chi Liu

Found 44 papers, 16 papers with code

GenRC: Generative 3D Room Completion from Sparse Image Collections

1 code implementation17 Jul 2024 Ming-Feng Li, Yueh-Feng Ku, Hong-Xuan Yen, Chi Liu, Yu-Lun Liu, Albert Y. C. Chen, Cheng-Hao Kuo, Min Sun

GenRC outperforms state-of-the-art methods under most appearance and geometric metrics on ScanNet and ARKitScenes datasets, even though GenRC is not trained on these datasets nor using predefined camera trajectories.

OphNet: A Large-Scale Video Benchmark for Ophthalmic Surgical Workflow Understanding

1 code implementation11 Jun 2024 Ming Hu, Peng Xia, Lin Wang, Siyuan Yan, Feilong Tang, Zhongxing Xu, Yimin Luo, Kaimin Song, Jurgen Leitner, Xuelian Cheng, Jun Cheng, Chi Liu, Kaijing Zhou, ZongYuan Ge

Existing datasets for surgical workflow analysis, which typically face challenges such as small scale, a lack of diversity in surgery and phase categories, and the absence of time-localized annotations, limit the requirements for action understanding and model generalization validation in complex and diverse real-world surgical scenarios.

Action Understanding Temporal Localization

LpQcM: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising

no code implementations27 Apr 2024 Menghua Xia, Huidong Xie, Qiong Liu, Bo Zhou, Hanzhong Wang, Biao Li, Axel Rominger, Kuangyu Shi, Georges EI Fakhri, Chi Liu

Specifically, the LpQcM consists of two components, the lesion-perceived modulation (LpM) and the multiscale quantification-consistent modulation (QcM).

Image Denoising

Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation

no code implementations6 Apr 2024 Yinchi Zhou, Tianqi Chen, Jun Hou, Huidong Xie, Nicha C. Dvornek, S. Kevin Zhou, David L. Wilson, James S. Duncan, Chi Liu, Bo Zhou

To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step.

Image-to-Image Translation Translation

Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT

1 code implementation23 Jan 2024 Xiongchao Chen, Bo Zhou, Xueqi Guo, Huidong Xie, Qiong Liu, James S. Duncan, Albert J. Sinusas, Chi Liu

Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ($\mu$-maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments.

Computed Tomography (CT) Denoising +1

Prompt-driven Latent Domain Generalization for Medical Image Classification

2 code implementations5 Jan 2024 Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatra, Brigid Betz-Stablein, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge

To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG).

Domain Generalization Image Classification +1

DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging

no code implementations7 Nov 2023 Huidong Xie, Weijie Gan, Bo Zhou, Xiongchao Chen, Qiong Liu, Xueqi Guo, Liang Guo, Hongyu An, Ulugbek S. Kamilov, Ge Wang, Chi Liu

We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models.

Image Denoising Medical Image Denoising

Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification

1 code implementation8 Sep 2023 Haochun Wang, Sendong Zhao, Chi Liu, Nuwa Xi, MuZhen Cai, Bing Qin, Ting Liu

Experimental results indicate that even without tuning any parameters, our LLE-INC is on par with automated verbalizers with parameter tuning.

TAI-GAN: Temporally and Anatomically Informed GAN for early-to-late frame conversion in dynamic cardiac PET motion correction

1 code implementation23 Aug 2023 Xueqi Guo, Luyao Shi, Xiongchao Chen, Bo Zhou, Qiong Liu, Huidong Xie, Yi-Hwa Liu, Richard Palyo, Edward J. Miller, Albert J. Sinusas, Bruce Spottiswoode, Chi Liu, Nicha C. Dvornek

The rapid tracer kinetics of rubidium-82 ($^{82}$Rb) and high variation of cross-frame distribution in dynamic cardiac positron emission tomography (PET) raise significant challenges for inter-frame motion correction, particularly for the early frames where conventional intensity-based image registration techniques are not applicable.

Generative Adversarial Network Image Registration +1

Transformer-based Dual-domain Network for Few-view Dedicated Cardiac SPECT Image Reconstructions

no code implementations18 Jul 2023 Huidong Xie, Bo Zhou, Xiongchao Chen, Xueqi Guo, Stephanie Thorn, Yi-Hwa Liu, Ge Wang, Albert Sinusas, Chi Liu

Our method aims to first reconstruct 3D cardiac SPECT images directly from projection data without the iterative reconstruction process by proposing a customized projection-to-image domain transformer.

Towards Robust GAN-generated Image Detection: a Multi-view Completion Representation

no code implementations2 Jun 2023 Chi Liu, Tianqing Zhu, Sheng Shen, Wanlei Zhou

GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes.

Joint Denoising and Few-angle Reconstruction for Low-dose Cardiac SPECT Using a Dual-domain Iterative Network with Adaptive Data Consistency

no code implementations17 May 2023 Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo, Qiong Liu, Albert J. Sinusas, Chi Liu

To overcome these challenges, we propose a dual-domain iterative network for end-to-end joint denoising and reconstruction from low-dose and few-angle projections of cardiac SPECT.


Cross-domain Iterative Network for Simultaneous Denoising, Limited-angle Reconstruction, and Attenuation Correction of Low-dose Cardiac SPECT

no code implementations17 May 2023 Xiongchao Chen, Bo Zhou, Huidong Xie, Xueqi Guo, Qiong Liu, Albert J. Sinusas, Chi Liu

Additionally, computed tomography (CT)-derived attenuation maps ($\mu$-maps) are commonly used for SPECT attenuation correction (AC), but it will cause extra radiation exposure and SPECT-CT misalignments.

Computed Tomography (CT) Denoising

Unified Noise-aware Network for Low-count PET Denoising

no code implementations28 Apr 2023 Huidong Xie, Qiong Liu, Bo Zhou, Xiongchao Chen, Xueqi Guo, Chi Liu

To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels.


HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge

1 code implementation14 Apr 2023 Haochun Wang, Chi Liu, Nuwa Xi, Zewen Qiang, Sendong Zhao, Bing Qin, Ting Liu

Large Language Models (LLMs), such as the LLaMA model, have demonstrated their effectiveness in various general-domain natural language processing (NLP) tasks.

Global Prompt Cell: A Portable Control Module for Effective Prompt Tuning

no code implementations12 Apr 2023 Chi Liu, Haochun Wang, Nuwa Xi, Sendong Zhao, Bing Qin

As a novel approach to tuning pre-trained models, prompt tuning involves freezing the parameters in downstream tasks while inserting trainable embeddings into inputs in the first layer.

EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition

1 code implementation4 Apr 2023 Siyuan Yan, Chi Liu, Zhen Yu, Lie Ju, Dwarikanath Mahapatrainst, Victoria Mar, Monika Janda, Peter Soyer, ZongYuan Ge

Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset.

Domain Generalization General Knowledge

FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising

1 code implementation2 Apr 2023 Bo Zhou, Huidong Xie, Qiong Liu, Xiongchao Chen, Xueqi Guo, Zhicheng Feng, Jun Hou, S. Kevin Zhou, Biao Li, Axel Rominger, Kuangyu Shi, James S. Duncan, Chi Liu

While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored.

Denoising Personalized Federated Learning

Meta-information-aware Dual-path Transformer for Differential Diagnosis of Multi-type Pancreatic Lesions in Multi-phase CT

no code implementations2 Mar 2023 Bo Zhou, Yingda Xia, Jiawen Yao, Le Lu, Jingren Zhou, Chi Liu, James S. Duncan, Ling Zhang

Accurate detection, segmentation, and differential diagnosis of the full taxonomy of pancreatic lesions, i. e., normal, seven major types of lesions, and other lesions, is critical to aid the clinical decision-making of patient management and treatment.

Classification Decision Making +2

Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction

no code implementations18 Feb 2023 Bo Zhou, Jo Schlemper, Neel Dey, Seyed Sadegh Mohseni Salehi, Kevin Sheth, Chi Liu, James S. Duncan, Michal Sofka

To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains.

MRI Reconstruction Self-Supervised Learning

Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words

1 code implementation COLING 2022 Haochun Wang, Chi Liu, Nuwa Xi, Sendong Zhao, Meizhi Ju, Shiwei Zhang, Ziheng Zhang, Yefeng Zheng, Bing Qin, Ting Liu

Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain.

Natural Language Inference

Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network

no code implementations13 Jun 2022 Xueqi Guo, Bo Zhou, David Pigg, Bruce Spottiswoode, Michael E. Casey, Chi Liu, Nicha C. Dvornek

The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information.

Motion Estimation

Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack

no code implementations22 Mar 2022 Chi Liu, Huajie Chen, Tianqing Zhu, Jun Zhang, Wanlei Zhou

To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies.

Face Swapping

Label uncertainty-guided multi-stream model for disease screening

no code implementations28 Jan 2022 Chi Liu, ZongYuan Ge, Mingguang He, Xiaotong Han

The main idea is dividing the images into simple and hard cases by uncertainty information, and then developing a multi-stream network to deal with different cases separately.

Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement

no code implementations28 Jul 2021 Juan Liu, Masoud Malekzadeh, Niloufar Mirian, Tzu-An Song, Chi Liu, Joyita Dutta

High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images.

Deblurring Image Denoising +1

Anatomy-Constrained Contrastive Learning for Synthetic Segmentation without Ground-truth

1 code implementation12 Jul 2021 Bo Zhou, Chi Liu, James S. Duncan

The manual efforts can be alleviated if the manual segmentation in one imaging modality (e. g., CT) can be utilized to train a segmentation network in another imaging modality (e. g., CBCT/MRI/PET).

Anatomy Contrastive Learning +1

Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration

1 code implementation14 Apr 2021 Bo Zhou, Zachary Augenfeld, Julius Chapiro, S. Kevin Zhou, Chi Liu, James S. Duncan

Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.

Anatomy Domain Adaptation +3

Simultaneous Denoising and Motion Estimation for Low-dose Gated PET using a Siamese Adversarial Network with Gate-to-Gate Consistency Learning

no code implementations14 Sep 2020 Bo Zhou, Yu-Jung Tsai, Chi Liu

With high-quality recovered gated volumes, gate-to-gate motion vectors can be simultaneously outputted from the motion estimation network.

Denoising Motion Estimation +1

Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram Consistency

no code implementations3 Sep 2020 Bo Zhou, S. Kevin Zhou, James S. Duncan, Chi Liu

To derive quality reconstruction, previous state-of-the-art methods use UNet-like neural architectures to directly predict the full view reconstruction from limited view data; but these methods leave the deep network architecture issue largely intact and cannot guarantee the consistency between the sinogram of the reconstructed image and the acquired sinogram, leading to a non-ideal reconstruction.

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

A Novel Loss Function Incorporating Imaging Acquisition Physics for PET Attenuation Map Generation using Deep Learning

no code implementations3 Sep 2019 Luyao Shi, John A. Onofrey, Enette Mae Revilla, Takuya Toyonaga, David Menard, Jo-seph Ankrah, Richard E. Carson, Chi Liu, Yihuan Lu

Recently, a convolutional neural network (CNN) was applied to predict the CT attenuation map ($\mu$-CNN) from $\lambda$-MLAA and $\mu$-MLAA, in which an image-domain loss (IM-loss) function between the $\mu$-CNN and the ground truth $\mu$-CT was used.

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