Publications

This study explores variations in velar consonant production across different phonological environments using dynamic magnetic resonance imaging (MRI).

F. Xing, J. Zhuo, M. Stone, X. Liu, T. G. Reese, V. J. Wedeen, J. L. Prince, J. Woo

JASA (2024)

In this work, we develop a tag-to-cine MRI sequence synthesis approach using spatio-temporal transformer.

X. Liu, F. Xing, Z. Bian, T. Arias-Vergara, P. Andrea Pérez-Toro, A. Maier, M. Stone, J. Zhuo, J. L. Prince and J. Woo

MICCAI (2024)

An off-the-shelf model for unsupervised domain adaptation (OSUDA) can be vulnerable to backdoor attacks due to the inability to access or control source domain data, but a proposed method quantifies backdoor sensitivity using a Lipschitz constant and eliminates the infection by overwriting backdoor-related channel kernels, validated on a multi-center cardiac dataset.

X. Liu, F. Xing, H. Gaggin, C.-C. J. Kuo, G. El Fakhri, J. Woo

IEEE ISBI (2024)

In this work, we develop an unsupervised domain adaptation approach to carry out class-wise conditional alignment.

X. Liu, F. Xing, J. You, J. Lu, C.-C. J. Kuo, G. El Fakhri, J. Woo

IEEE Transactions on Neural Networks and Learning Systems (TNNLS) (2024)

Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing.

Z. Bian, A. Alshareef, S. Wei, J. Chen, Y. Wang, J. Woo, D. L. Pham, J. Zhuo, A. Carass, J. L. Prince

SPIE Medical Imaging (2024) – Best Student Paper Award

Magnetic Resonance Imaging with tagging (tMRI) has long been utilized for quantifying tissue motion and strain during deformation. However, a phenomenon known as tag fading, a gradual decrease in tag visibility over time, often complicates post-processing.

J. Cho, X. Liu, F. Xing, J. Ouyang, G. El Fakhri, J. Park, J. Woo

SPIE Medical Imaging (2024)

With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language models (LLMs) have received much attention nowadays. A comprehensive overview on bias and fairness in chatbot systems is given in this paper.

J. Xue, Y.-C. Wang, C. Wei, X. Liu, J. Woo, C.-C. J. Kuo

APSIPA Trans on Signal and Information Processing (2024)

This study investigates the potential role of 18F-FDG PET in reducing intra- and inter-reader variability thereby improving reproducibility of GTV delineation in STS, without incurring additional costs or radiation exposure.

E. Najem, T. Marin, Y. Zhuo, R. Maria Lahoud, F. Tian, A. Beddok, L. Rozenblum, F. Xing, M. Moteabbed, R. Lim, X. Liu, J. Woo, S. John Lostetter, A. Lamane, Y.-L. Chen, C. Ma, G. El Fakhri

Radiotherapy and Oncology (2024)

In this work, we develop a new deep learning-based image denoising especially to deal with rapid fluorescence and ultrasound captures where traditional noise mitigation strategies are limited, such as increasing pixel dwell time or frame averaging.

K. Lee, H. Lee, M. Lee, J. Chang, C.-C. Jay Kuo, S.-J. Oh, J. Woo, J. Y. Hwang

APSIPA Trans on Signal and Information Processing (2024)

In this work, we develop a noise-robust sleep stage network via adversarial training with an auxiliary model.

C. H. Yoo, X. Liu, F. Xing, G. El Fakhri, J. Woo, J.-W. Kang

IEEE Trans on Biomedical Engineering (2023)

In this work, we seek to progressively evolve an “off-the-shelf” trained segmentation model to diverse datasets with additional anatomical categories in a unified manner.

X. Liu, H. A. Shih, F. Xing, E. Santarnecchi, G. El Fakhri, J. Woo

MICCAI (2023)

The Stochastic Confidence Network (SCN) is a novel deep learning model designed to handle outliers in medical imaging, ensuring accurate and robust diagnoses. Experimental results on databases for breast tumors and Alzheimer’s disease demonstrated that SCN significantly improves diagnostic performance by eliminating irrelevant outlier data and resampling outliers into a typical distribution, outperforming state-of-the-art models.

K. Lee, H. Lee, G. El Fakhri, J. Sepulcre, X. Liu, F. Xing, J. Y. Hwang, J. Woo

MICCAI MTSAIL (2023)

We introduce a highly innovative imaging method to study the complex velopharyngeal system and introduce the potential future clinical applications of a VP atlas in cleft care.

J. Perry, I. Gilbert, F. Xing, R. Jin, D. Kuehn, R. Shosted, J. Woo, Z. P. Liang, B. Sutton

The Cleft Palate Craniofacial Journal (2023)

We developed successive subspace learning for cardiac disease classification from cine MRI.

X. Liu, F. Xing, H. K. Gaggin, C.-C. J. Kuo, G. El Fakhri, J. Woo

IEEE ISBI (2023)

We developed a new self-supervised domain adaptive segmentation method for breast cancer.

K. Lee, H. Lee, G. El Fakhri, J. Woo, J. Y. Hwang

MICCAI (2023)

We propose off-the-shelf (OS) UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor trained in a source domain to a target domain, in the absence of source domain data in adaptation.

X. Liu, F. Xing, G. El Fakhri, J. Woo

Medical Image Analysis (2023)

We improved the spatiotemporal qualities of images and dynamics of speech MRI through an improved data sampling and image reconstruction approach.

R. Jin, Y. Li, R. K. Shosted, F. Xing, I. Gilbert, J. L. Perry, J. Woo, Z.-P. Liang, B. P. Sutton

Magnetic Resonance in Medicine (2023)

In this work, we developed a generative self-training framework for domain adaptive image translation with continuous value prediction and regression objectives.

X. Liu, J. L. Prince, F. Xing, J. Zhuo, T. Reese, M. Stone, G. El Fakhria, J. Woo

Medical Image Analysis (2023)

In this work, we developed a speech audio synthesis approach from tagged MRI via Plastic transformer.

X. Liu, F. Xing, M. Stone, J. Zhuo, S. Fels, J. L. Prince, G. El Fakhri, J. Woo

MICCAI (2023)

In this work, we developed a federated learning approach for personalized skin diagnosis via fine-tuning network.

K. Lee, H. Lee, T. Coutinho Cavalcanti, S. Kim, G. El Fakhri, D. H. Lee, J. Woo, J. Y. Hwang

MICCAI (2023)

We use inverse atlas tongue modeling for investigating motor control similarity.

U. Maity, F. Xing, J. Prince, M. Stone, G. El Fakhri, J. Woo, S. Fels

Interspeech (2023)

In this work, we developed a method for deep learning based inpainting and applied to construct brain atlases.

F. Xing, X. Liu, C.-C. J. Kuo, G. El Fakhri, J. Woo

IEEE J. Biomed. Health Informatics (2022)

Unsupervised domain adaptation (UDA) transfers knowledge from a labeled source domain to unlabeled target domains, addressing the challenge of labeling in new domains. To overcome privacy concerns in cross-center collaborations, we propose a UDA framework using a black-box segmentation model with knowledge distillation and unsupervised entropy minimization, validated on multiple datasets and demonstrating potential in clinical settings.

X. Liu, C. Yoo, F. Xing, C.-C. J. Kuo, G. El Fakhri, J.-W. Kang and J. Woo

Frontiers in Neuroscience (2022)

In this work, we developed a lightweight and interpretable machine learning framework for ALS disease classification from structural MRI.

X. Liu, F. Xing, C. Yang, C.-C. J. Kuo, S. Babu, G. El Fakhri, T. Jenkins, J. Woo

IEEE J. Biomed. Health Informatics (2022)

This review summairzes state-of-the-art deep unsupervised domain adaptation techniques that are proposed in recent years.

X. Liu, C. Yoo, F. Xing, H. Oh, G. El Fakhri, J.-W. Kang, J. Woo

APSIPA Trans on Signal and Information Processing (2022)

This work proposed an adversarial unsupervised domain adaptation with conditional and label shift.

X. Liu, Z. Guo, S. Li, F. Xing, J. You, C.-C. J. Kuo, G. El Fakhri, J. Woo

ICCV (2021)

This work proposed severity-aware semantic segmentation with reinforced Wasserstein training.

X. Liu, W. Ji, J. You, G. El Fakhri, J. Woo

CVPR (2020)