25 citations · 96 across the 27 of their papers we have counts for
28 papers
A Speech-to-Video Synthesis Approach Using Spatio-Temporal Diffusion for Vocal Tract MRI
Paula Andrea Pérez-Toro, Tomás Arias-Vergara, Fangxu Xing +9
Understanding the relationship between vocal tract motion during speech and the resulting acoustic signal is crucial for aided clinical assessment and developing personalized treat…
Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRI
Xiaofeng Liu, Fangxu Xing, Jiachen Zhuo +4
Understanding the relationship between tongue motion patterns during speech and their resulting speech acoustic outcomes -- i.e., articulatory-acoustic relation -- is of great impo…
Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI
Xiaofeng Liu, Nadya Shusharina, Helen A Shih +3
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. The perso…
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser
Jihoon Cho, Xiaofeng Liu, Fangxu Xing +4
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 mul…
Posterior Estimation for Dynamic PET imaging using Conditional Variational Inference
Xiaofeng Liu, Thibault Marin, Tiss Amal +3
This work aims efficiently estimating the posterior distribution of kinetic parameters for dynamic positron emission tomography (PET) imaging given a measurement of time of activit…
Speech Audio Synthesis from Tagged MRI and Non-Negative Matrix Factorization via Plastic Transformer
Xiaofeng Liu, Fangxu Xing, Maureen Stone +5
The tongue's intricate 3D structure, comprising localized functional units, plays a crucial role in the production of speech. When measured using tagged MRI, these functional units…
Bias and Fairness in Chatbots: An Overview
Jintang Xue, Yun-Cheng Wang, Chengwei Wei +3
Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language…
Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI
Xiaofeng Liu, Helen A. Shih, Fangxu Xing +3
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. Yet, the static…
Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translation
Xiaofeng Liu, Jerry L. Prince, Fangxu Xing +5
Self-training is an important class of unsupervised domain adaptation (UDA) approaches that are used to mitigate the problem of domain shift, when applying knowledge learned from a…
Posterior Estimation Using Deep Learning: A Simulation Study of Compartmental Modeling in Dynamic PET
Xiaofeng Liu, Thibault Marin, Tiss Amal +3
Background: In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored. Purpose: This work aims at using deep learning to…
Synthesizing audio from tongue motion during speech using tagged MRI via transformer
Xiaofeng Liu, Fangxu Xing, Jerry L. Prince +3
Investigating the relationship between internal tissue point motion of the tongue and oropharyngeal muscle deformation measured from tagged MRI and intelligible speech can aid in a…
Successive Subspace Learning for Cardiac Disease Classification with Two-phase Deformation Fields from Cine MRI
Xiaofeng Liu, Fangxu Xing, Hanna K. Gaggin +3
Cardiac cine magnetic resonance imaging (MRI) has been used to characterize cardiovascular diseases (CVD), often providing a noninvasive phenotyping tool.~While recently flourished…
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation
Xiaofeng Liu, Fangxu Xing, Georges El Fakhri +1
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 heter…
Unsupervised Domain Adaptation for Segmentation with Black-box Source Model
Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing +3
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…
Feasibility study of clinical target volume definition for soft-tissue sarcoma using muscle fiber orientations derived from diffusion tensor imaging
Nadya Shusharina, Xiaofeng Liu, Jaume Coll-Font +5
Objective: Soft-tissue sarcoma spreads preferentially along muscle fibers. We explore the utility of deriving muscle fiber orientations from diffusion tensor MRI (DT-MRI) for defin…
Tagged-MRI Sequence to Audio Synthesis via Self Residual Attention Guided Heterogeneous Translator
Xiaofeng Liu, Fangxu Xing, Jerry L. Prince +4
Understanding the underlying relationship between tongue and oropharyngeal muscle deformation seen in tagged-MRI and intelligible speech plays an important role in advancing speech…
Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement
Xiaofeng Liu, Fangxu Xing, Jerry L. Prince +3
Cycle reconstruction regularized adversarial training -- e.g., CycleGAN, DiscoGAN, and DualGAN -- has been widely used for image style transfer with unpaired training data. Several…
Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures
Xiaofeng Liu, Fangxu Xing, Thibault Marin +2
Lesions or organ boundaries visible through medical imaging data are often ambiguous, thus resulting in significant variations in multi-reader delineations, i.e., the source of ale…
Deep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas
Thibault Marin, Yue Zhuo, Rita Maria Lahoud +11
Background and purpose: The delineation of the gross tumor volume (GTV) is a critical step for radiation therapy treatment planning. The delineation procedure is typically performe…
Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
Xiaofeng Liu, Zhenhua Guo, Site Li +5
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…
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference
Xiaofeng Liu, Bo Hu, Linghao Jin +6
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…
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI
Xiaofeng Liu, Fangxu Xing, Hanna K. Gaggin +4
Assessment of cardiovascular disease (CVD) with cine magnetic resonance imaging (MRI) has been used to non-invasively evaluate detailed cardiac structure and function. Accurate seg…
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis
Xiaofeng Liu, Fangxu Xing, Maureen Stone +5
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 d…
Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation
Xiaofeng Liu, Fangxu Xing, Chao Yang +2
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled and unseen target domain, which is usually trained on data from…