no code implementations • 10 Feb 2024 • Xiaofeng Liu, Nadya Shusharina, Helen A Shih, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans.
no code implementations • 10 Feb 2024 • Xiaofeng Liu, Fangxu Xing, Jiachen Zhuo, Maureen Stone, Jerry L. Prince, Georges El Fakhri, Jonghye Woo
In this work, we aim to develop a framework for detecting speech motion anomalies in conjunction with their corresponding speech acoustics.
no code implementations • 1 Feb 2024 • Jihoon Cho, Xiaofeng Liu, Fangxu Xing, Jinsong Ouyang, Georges El Fakhri, Jinah Park, Jonghye Woo
Multimodal Magnetic Resonance (MR) Imaging plays a crucial role in disease diagnosis due to its ability to provide complementary information by analyzing a relationship between multimodal images on the same subject.
no code implementations • 24 Oct 2023 • Xiaofeng Liu, Thibault Marin, Tiss Amal, Jonghye Woo, Georges El Fakhri, Jinsong Ouyang
Specifically, we counteract the information loss in the forward process by introducing latent variables.
no code implementations • 26 Sep 2023 • Xiaofeng Liu, Fangxu Xing, Maureen Stone, Jiachen Zhuo, Sidney Fels, Jerry L. Prince, Georges El Fakhri, Jonghye Woo
The tongue's intricate 3D structure, comprising localized functional units, plays a crucial role in the production of speech.
no code implementations • 21 Jun 2023 • Se-In Jang, Cristina Lois, Emma Thibault, J. Alex Becker, Yafei Dong, Marc D. Normandin, Julie C. Price, Keith A. Johnson, Georges El Fakhri, Kuang Gong
Preliminary experimental results based on clinical [18F]MK-6240 datasets demonstrate the feasibility of the proposed method in generating realistic tau PET images at different clinical stages.
no code implementations • 30 May 2023 • Xiaofeng Liu, Helen A. Shih, Fangxu Xing, Emiliano Santarnecchi, Georges El Fakhri, Jonghye Woo
Deep learning (DL) models for segmenting various anatomical structures have achieved great success via a static DL model that is trained in a single source domain.
no code implementations • 23 May 2023 • Xiaofeng Liu, Jerry L. Prince, Fangxu Xing, Jiachen Zhuo, Reese Timothy, Maureen Stone, Georges El Fakhri, Jonghye Woo
We evaluated our framework on two cross-scanner/center, inter-subject translation tasks, including tagged-to-cine magnetic resonance (MR) image translation and T1-weighted MR-to-fractional anisotropy translation.
no code implementations • 17 Mar 2023 • Xiaofeng Liu, Thibault Marin, Tiss Amal, Jonghye Woo, Georges El Fakhri, Jinsong Ouyang
Purpose: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties.
no code implementations • 14 Feb 2023 • Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Maureen Stone, Georges El Fakhri, Jonghye Woo
However, elucidating the relationship between these two sources of information is challenging, due in part to the disparity in data structure between spatiotemporal motion fields (i. e., 4D motion fields) and one-dimensional audio waveforms.
no code implementations • 21 Jan 2023 • Xiaofeng Liu, Fangxu Xing, Hanna K. Gaggin, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool.~While recently flourished deep learning based approaches using cine MRI yield accurate characterization results, the performance is often degraded by small training samples.
no code implementations • 16 Sep 2022 • Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo
Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to facilitate the implementation in an unlabeled heterogeneous target domain.
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 • 16 Aug 2022 • Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
Unsupervised domain adaptation (UDA) has been widely used to transfer knowledge from a labeled source domain to an unlabeled target domain to counter the difficulty of labeling in a new domain.
no code implementations • 16 Aug 2022 • Xiaofeng Liu, Fangxu Xing, Jia You, Jun Lu, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying fine-grained subtype structure and the cross-domain within-class compactness have not been fully investigated.
no code implementations • 15 Aug 2022 • Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, Hyejin Oh, Georges El Fakhri, Je-Won Kang, Jonghye Woo
Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain.
no code implementations • 5 Jun 2022 • Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Jiachen Zhuo, Maureen Stone, Georges El Fakhri, Jonghye Woo
Understanding the underlying relationship between tongue and oropharyngeal muscle deformation seen in tagged-MRI and intelligible speech plays an important role in advancing speech motor control theories and treatment of speech related-disorders.
no code implementations • 5 Jun 2022 • Xiaofeng Liu, Fangxu Xing, Nadya Shusharina, Ruth Lim, C-C Jay Kuo, Georges El Fakhri, Jonghye Woo
Unsupervised domain adaptation (UDA) has been vastly explored to alleviate domain shifts between source and target domains, by applying a well-performed model in an unlabeled target domain via supervision of a labeled source domain.
no code implementations • 25 Feb 2022 • Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Maureen Stone, Georges El Fakhri, Jonghye Woo
Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities.
no code implementations • 18 Jan 2022 • Xiaofeng Liu, Fangxu Xing, Thibault Marin, Georges El Fakhri, Jonghye Woo
Then, we apply a variational autoencoder network and optimize its evidence lower bound (ELBO) to efficiently approximate the distribution of the segmentation map, given an MR image.
no code implementations • 13 Jan 2022 • Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo
Unsupervised domain adaptation (UDA) between two significantly disparate domains to learn high-level semantic alignment is a crucial yet challenging task.~To this end, in this work, we propose exploiting low-level edge information to facilitate the adaptation as a precursor task, which has a small cross-domain gap, compared with semantic segmentation.~The precise contour then provides spatial information to guide the semantic adaptation.
no code implementations • ICCV 2021 • Xiaofeng Liu, Zhenhua Guo, Site Li, Fangxu Xing, Jane You, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w. r. t.
no code implementations • 22 Jul 2021 • Xiaofeng Liu, Fangxu Xing, Hanna K. Gaggin, Weichung Wang, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function.
no code implementations • 22 Jul 2021 • Xiaofeng Liu, Bo Hu, Linghao Jin, Xu Han, Fangxu Xing, Jinsong Ouyang, Jun Lu, Georges El Fakhri, Jonghye Woo
In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training.
no code implementations • 23 Jun 2021 • Xiaofeng Liu, Fangxu Xing, Chao Yang, Georges El Fakhri, Jonghye Woo
To alleviate this, in this work, we target source free UDA for segmentation, and propose to adapt an ``off-the-shelf" segmentation model pre-trained in the source domain to the target domain, with an adaptive batch-wise normalization statistics adaptation framework.
no code implementations • 23 Jun 2021 • Xiaofeng Liu, Fangxu Xing, Maureen Stone, Jiachen Zhuo, Reese Timothy, Jerry L. Prince, Georges El Fakhri, Jonghye Woo
Self-training based unsupervised domain adaptation (UDA) has shown great potential to address the problem of domain shift, when applying a trained deep learning model in a source domain to unlabeled target domains.
no code implementations • 14 Jan 2021 • Xiaofeng Liu, Fangxu Xing, Georges El Fakhri, Jonghye Woo
Our framework hinges on a cycle-constrained conditional adversarial training approach, where it can extract a modality-invariant anatomical feature with a modality-agnostic encoder and generate a target modality with a conditioned decoder.
no code implementations • 14 Jan 2021 • Xiaofeng Liu, Fangxu Xing, Jerry L. Prince, Aaron Carass, Maureen Stone, Georges El Fakhri, Jonghye Woo
Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs.
no code implementations • 13 Jan 2021 • Xiaofeng Liu, Fangxu Xing, Chao Yang, C. -C. Jay Kuo, Suma Babu, Georges El Fakhri, Thomas Jenkins, Jonghye Woo
Deep learning has great potential for accurate detection and classification of diseases with medical imaging data, but the performance is often limited by the number of training datasets and memory requirements.
no code implementations • 1 Jan 2021 • Xiaofeng Liu, Xiongchang Liu, Bo Hu, Wenxuan Ji, Fangxu Xing, Jun Lu, Jane You, C. -C. Jay Kuo, Georges El Fakhri, Jonghye Woo
Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the closeness of cross-domain class centroids.
no code implementations • 9 Jul 2020 • Jonghye Woo, Fangxu Xing, Jerry L. Prince, Maureen Stone, Arnold Gomez, Timothy G. Reese, Van J. Wedeen, Georges El Fakhri
Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability.
no code implementations • 15 Apr 2018 • Jonghye Woo, Jerry L. Prince, Maureen Stone, Fangxu Xing, Arnold Gomez, Jordan R. Green, Christopher J. Hartnick, Thomas J. Brady, Timothy G. Reese, Van J. Wedeen, Georges El Fakhri
We then use three-dimensional synthetic and \textit{in vivo} tongue motion data using protrusion and simple speech tasks to identify subject-specific and data-driven functional units of the tongue in localized regions.
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.
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 • 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).
no code implementations • 24 Jan 2017 • Jonghye Woo, Fangxu Xing, Maureen Stone, Jordan Green, Timothy G. Reese, Thomas J. Brady, Van J. Wedeen, Jerry L. Prince, Georges El Fakhri
Quantitative measurement of functional and anatomical traits of 4D tongue motion in the course of speech or other lingual behaviors remains a major challenge in scientific research and clinical applications.