no code implementations • 21 Dec 2022 • Ye Li, Junyu Chen, Se-In Jang, Kuang Gong, Quanzheng Li
Inspired by the recent success of Transformers for Natural Language Processing and vision Transformer for Computer Vision, many researchers in the medical imaging community have flocked to Transformer-based networks for various main stream medical tasks such as classification, segmentation, and estimation.
no code implementations • 7 Nov 2022 • Arman Rahmim, Tyler J. Bradshaw, Irène Buvat, Joyita Dutta, Abhinav K. Jha, Paul E. Kinahan, Quanzheng Li, Chi Liu, Melissa D. McCradden, Babak Saboury, Eliot Siegel, John J. Sunderland, Richard L. Wahl
The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022.
no code implementations • 5 Nov 2022 • Homgmin Cai, Wenxiong Liao, Zhengliang Liu, Xiaoke Huang, Yiyang Zhang, Siqi Ding, Sheng Li, Quanzheng Li, Tianming Liu, Xiang Li
In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation.
no code implementations • 13 Sep 2022 • Kuang Gong, Keith A. Johnson, Georges El Fakhri, Quanzheng Li, Tinsu Pan
Regional and surface quantification shows that employing MR prior as the network input while embedding PET image as a data-consistency constraint during inference can achieve the best performance.
no code implementations • 8 Sep 2022 • Aoxiao Zhong, Hao He, Zhaolin Ren, Na Li, Quanzheng Li
To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on personalizing models for clients.
no code implementations • 7 Sep 2022 • Se-In Jang, Tinsu Pan, Ye Li, Pedram Heidari, Junyu Chen, Quanzheng Li, Kuang Gong
In this work, we proposed an efficient spatial and channel-wise encoder-decoder transformer, Spach Transformer, that can leverage spatial and channel information based on local and global MSAs.
no code implementations • 15 Mar 2022 • Ye Li, Jianan Cui, Junyu Chen, Guodong Zeng, Scott Wollenweber, Floris Jansen, Se-In Jang, Kyungsang Kim, Kuang Gong, Quanzheng Li
Our hypothesis is that by explicitly providing the local relative noise level of the input image to a deep convolutional neural network (DCNN), the DCNN can outperform itself trained on image appearance only.
1 code implementation • 5 Mar 2022 • Yutong Xie, Quanzheng Li
We propose a novel and unified method, measurement-conditioned denoising diffusion probabilistic model (MC-DDPM), for under-sampled medical image reconstruction based on DDPM.
1 code implementation • 8 Feb 2022 • Yutong Xie, Dufan Wu, Bin Dong, Quanzheng Li
We proved that a trained model in supervised deep learning minimizes the conditional risk for each input (Theorem 2. 1).
no code implementations • 1 Feb 2022 • Jin-Hyun Ahn, Kyungsang Kim, Jeongwan Koh, Quanzheng Li
Federated learning (FL) has been intensively investigated in terms of communication efficiency, privacy, and fairness.
no code implementations • 18 Jun 2021 • Kuang Gong, Ciprian Catana, Jinyi Qi, Quanzheng Li
Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms by combining the PET imaging model and tracer kinetics in an integrated framework.
no code implementations • 8 Apr 2021 • Mo Zhang, Fei Yu, Jie Zhao, Li Zhang, Quanzheng Li
Blood vessel segmentation is crucial for many diagnostic and research applications.
no code implementations • 21 Mar 2021 • Varun Buch, Aoxiao Zhong, Xiang Li, Marcio Aloisio Bezerra Cavalcanti Rockenbach, Dufan Wu, Hui Ren, Jiahui Guan, Andrew Liteplo, Sayon Dutta, Ittai Dayan, Quanzheng Li
Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance.
no code implementations • 1 Jan 2021 • Yutong Xie, Gaoxiang Chen, Quanzheng Li
Inspired by the proof of this upper bound and the framework of matrix computation in \citet{hinz2019framework}, we propose a general computational approach to compute a tight upper bound of regions number for theoretically any network structures (e. g. DNN with all kind of skip connections and residual structures).
no code implementations • 29 Dec 2020 • Mo Zhang, Quanzheng Li
It is crucial to take multi-scale information of tissue structure into account in the detection of breast cancer.
no code implementations • 23 Dec 2020 • Wei Qiu, Yangsibo Huang, Quanzheng Li
Missing value imputation is a challenging and well-researched topic in data mining.
no code implementations • 8 Dec 2020 • Yutong Xie, Gaoxiang Chen, Quanzheng Li
Inspired by the proof of this upper bound and theframework of matrix computation in Hinz & Van de Geer (2019), we propose ageneral computational approach to compute a tight upper bound of regions numberfor theoretically any network structures (e. g. DNN with all kind of skip connec-tions and residual structures).
no code implementations • 26 Nov 2020 • Aoxiao Zhong, Xiang Li, Dufan Wu, Hui Ren, Kyungsang Kim, YoungGon Kim, Varun Buch, Nir Neumark, Bernardo Bizzo, Won Young Tak, Soo Young Park, Yu Rim Lee, Min Kyu Kang, Jung Gil Park, Byung Seok Kim, Woo Jin Chung, Ning Guo, Ittai Dayan, Mannudeep K. Kalra, Quanzheng Li
These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.
1 code implementation • 26 Sep 2020 • Young-Gon Kim, Kyungsang Kim, Dufan Wu, Hui Ren, Won Young Tak, Soo Young Park, Yu Rim Lee, Min Kyu Kang, Jung Gil Park, Byung Seok Kim, Woo Jin Chung, Mannudeep K. Kalra, Quanzheng Li
A segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used, which are the clinical landmarks to separate the upper and lower lungs.
no code implementations • 14 Sep 2020 • Jianan Cui, Kuang Gong, Paul Han, Huafeng Liu, Quanzheng Li
After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer.
no code implementations • 13 Sep 2020 • Nuobei Xie, Kuang Gong, Ning Guo, Zhixing Qin, Jianan Cui, Zhifang Wu, Huafeng Liu, Quanzheng Li
Patlak model is widely used in 18F-FDG dynamic positron emission tomography (PET) imaging, where the estimated parametric images reveal important biochemical and physiology information.
no code implementations • 22 May 2020 • Dufan Wu, Daniel Montes, Ziheng Duan, Yangsibo Huang, Javier M. Romero, Ramon Gilberto Gonzalez, Quanzheng Li
Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network.
no code implementations • 19 May 2020 • Dufan Wu, Hui Ren, Quanzheng Li
It is necessary to reduce the dose of CTP for routine applications due to the high radiation exposure from the repeated scans, where image denoising is necessary to achieve a reliable diagnosis.
no code implementations • 8 May 2020 • Mengjia Xu, David Lopez Sanz, Pilar Garces, Fernando Maestu, Quanzheng Li, Dimitrios Pantazis
Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms.
no code implementations • 16 Dec 2019 • Nuobei Xie, Kuang Gong, Ning Guo, Zhixin Qin, Zhifang Wu, Huafeng Liu, Quanzheng Li
Positron emission tomography (PET) is widely used for clinical diagnosis.
no code implementations • 7 Oct 2019 • Wei Qiu, Jiaming Guo, Xiang Li, Mengjia Xu, Mo Zhang, Ning Guo, Quanzheng Li
As the six networks are trained with image patches consisting of both individual cells and touching/overlapping cells, they can effectively recognize cell types that are presented in multi-instance image samples.
no code implementations • 1 Oct 2019 • Jiaming Guo, Wei Qiu, Xiang Li, Xuandong Zhao, Ning Guo, Quanzheng Li
Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET).
no code implementations • 26 Jul 2019 • Fei Yu, Jie Zhao, Yanjun Gong, Zhi Wang, Yuxi Li, Fan Yang, Bin Dong, Quanzheng Li, Li Zhang
Segmenting coronary arteries is challenging, as classic unsupervised methods fail to produce satisfactory results and modern supervised learning (deep learning) requires manual annotation which is often time-consuming and can some time be infeasible.
no code implementations • 7 Jul 2019 • Mo Zhang, Jie Zhao, Xiang Li, Li Zhang, Quanzheng Li
Such pixel-level dilation rates produce optimal receptive fields so that the information of objects with different sizes can be extracted at the corresponding scale.
no code implementations • 9 Jun 2019 • Dufan Wu, Kuang Gong, Kyungsang Kim, Quanzheng Li
In this paper we proposed a training method which learned denoising neural networks from noisy training samples only.
1 code implementation • 3 Dec 2018 • Jie Zhao, Quanzheng Li, Xiang Li, Hongfeng Li, Li Zhang
Pap smear testing has been widely used for detecting cervical cancers based on the morphology properties of cell nuclei in microscopic image.
no code implementations • 5 Oct 2018 • Dufan Wu, Kyungsang Kim, Quanzheng Li
The purpose of this work is to reduce the memory and time consumption of the training of the reconstruction networks for CT to make it practical for current hardware, while maintaining the quality of the reconstructed images.
no code implementations • 1 Oct 2018 • Xiang Li, Qitian Chen, Xing Wang, Ning Guo, Nan Wu, Quanzheng Li
In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems.
no code implementations • 6 Aug 2018 • Jiasha Liu, Xiang Li, Hui Ren, Quanzheng Li
The framework combines two 1st-level modules: direct estimation module and a segmentation module.
no code implementations • 4 Jul 2018 • Kuang Gong, Kyungsang Kim, Jianan Cui, Ning Guo, Ciprian Catana, Jinyi Qi, Quanzheng Li
The representation is expressed using a deep neural network with the patient's prior images as network input.
no code implementations • 31 May 2018 • Yu Zhao, Xiang Li, Wei zhang, Shijie Zhao, Milad Makkie, Mo Zhang, Quanzheng Li, Tianming Liu
Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis.
no code implementations • ICLR 2018 • Dufan Wu, Kyungsang Kim, Bin Dong, Quanzheng Li
To align the acquisition with the annotations made by radiologists in the image domain, a DNN was built as the unrolled version of iterative reconstruction algorithms to map the acquisitions to images, and followed by a 3D convolutional neural network (CNN) to detect the abnormality in the reconstructed images.
no code implementations • 17 Dec 2017 • Kuang Gong, Jaewon Yang, Kyungsang Kim, Georges El Fakhri, Youngho Seo, Quanzheng Li
With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods.
no code implementations • 6 Nov 2017 • Dufan Wu, Kyungsang Kim, Bin Dong, Georges El Fakhri, Quanzheng Li
With 144 multi-slice fanbeam pro-jections, the proposed end-to-end detector could achieve comparable sensitivity with the reference detector, which was trained and applied on the fully-sampled image data.
no code implementations • 31 Oct 2017 • Zhe Guo, Xiang Li, Heng Huang, Ning Guo, Quanzheng Li
Image analysis using more than one modality (i. e. multi-modal) has been increasingly applied in the field of biomedical imaging.
no code implementations • ICML 2018 • Yiping Lu, Aoxiao Zhong, Quanzheng Li, Bin Dong
We show that many effective networks, such as ResNet, PolyNet, FractalNet and RevNet, can be interpreted as different numerical discretizations of differential equations.
no code implementations • 23 Oct 2017 • Mo Zhang, Xiang Li, Mengjia Xu, Quanzheng Li
Reliable cell segmentation and classification from biomedical images is a crucial step for both scientific research and clinical practice.
1 code implementation • 9 Oct 2017 • Kuang Gong, Jiahui Guan, Kyungsang Kim, Xuezhu Zhang, Georges El Fakhri, Jinyi Qi, Quanzheng Li
An innovative feature of the proposed method is that we embed the neural network in the iterative reconstruction framework for image representation, rather than using it as a post-processing tool.
no code implementations • 21 Jul 2017 • Seongah Jeong, Xiang Li, Jiarui Yang, Quanzheng Li, Vahid Tarokh
In order to address the limitations of the unsupervised DLSC-based fMRI studies, we utilize the prior knowledge of task paradigm in the learning step to train a data-driven dictionary and to model the sparse representation.
no code implementations • 19 Jul 2017 • Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li
However, the development of a robust and reliable deep learning model for computer-aided diagnosis is still highly challenging due to the combination of the high heterogeneity in the medical images and the relative lack of training samples.
no code implementations • 29 May 2017 • Songting Shi, Xiang Li, Arkadiusz Sitek, Quanzheng Li
In this article, we derive a Bayesian model to learning the sparse and low rank PARAFAC decomposition for the observed tensor with missing values via the elastic net, with property to find the true rank and sparse factor matrix which is robust to the noise.
no code implementations • 11 May 2017 • Dufan Wu, Kyungsang Kim, Georges El Fakhri, Quanzheng Li
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT).