Search Results for author: Georges El Fakhri

Found 16 papers, 1 papers with code

Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate

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.

Unsupervised Domain Adaptation

Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference

no code implementations22 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.

Bayesian Inference Domain Generalization

Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI

no code implementations22 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.

Dimensionality Reduction Feature Selection +1

Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation

no code implementations23 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.

Medical Image Segmentation Unsupervised Domain Adaptation

Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis

no code implementations23 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.

Image Generation Unsupervised Domain Adaptation +1

A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation

no code implementations14 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.

Translation Tumor Segmentation

VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI

no code implementations13 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.

Classification Dimensionality Reduction +1

Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis

no code implementations1 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.

Medical Diagnosis Unsupervised Domain Adaptation

A Deep Joint Sparse Non-negative Matrix Factorization Framework for Identifying the Common and Subject-specific Functional Units of Tongue Motion During Speech

no code implementations9 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.

A Sparse Non-negative Matrix Factorization Framework for Identifying Functional Units of Tongue Behavior from MRI

no code implementations15 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.

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images

no code implementations17 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.

Image Reconstruction

End-to-end Lung Nodule Detection in Computed Tomography

no code implementations6 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.

Computed Tomography (CT) Fine-tuning +1

Iterative PET Image Reconstruction Using Convolutional Neural Network Representation

1 code implementation9 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.

Denoising Image Reconstruction

A Cascaded Convolutional Neural Network for X-ray Low-dose CT Image Denoising

no code implementations11 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).

Computed Tomography (CT) Image Denoising

Speech Map: A Statistical Multimodal Atlas of 4D Tongue Motion During Speech from Tagged and Cine MR Images

no code implementations24 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.

Motion Estimation

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