Search Results for author: Chi Liu

Found 27 papers, 10 papers with code

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

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.


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, 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 +1

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

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 +2

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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