25 citations · 105 across the 38 of their papers we have counts for
42 papers
Exothermic nuclear recoils from thermal quasi-pNGB dark matter
Riasat Sheikh, Takashi Toma, Koji Tsumura
We study a two-component dark matter scenario comprising a stable pseudo-Nambu-Goldstone boson (pNGB) and its nearly degenerate long-lived quasi-pNGB partner. An approximate scalar…
One Frame, Full Heartbeat: ECG-Free 4D Cardiac Cine MRI Synthesis via Radial-Decomposed Flow Matching
Shiyi Wang, Ruochen Sun, Xiang Li +2
Cine cardiovascular magnetic resonance (CMR) captures the cardiac cycle as a four-dimensional (4D) sequence, but standard acquisition requires electrocardiogram (ECG) gating and re…
Speech-Guided Multimodal Learning for Vocal Tract Segmentation in Real-Time MRI
Daiqi Liu, Lukas Mulzer, Md Hasan +11
Segmenting vocal tract articulators in real-time MRI (rtMRI) is a challenging dynamic image segmentation problem characterized by low contrast, rapid motion, and limited spatial re…
Cross-Modal Fine-Tuning of 3D Convolutional Foundation Models for ADHD Classification with Low-Rank Adaptation
Jyun-Ping Kao, Shinyeong Rho, Shahar Lazarev +5
Early diagnosis of attention-deficit/hyperactivity disorder (ADHD) in children plays a crucial role in improving outcomes in education and mental health. Diagnosing ADHD using neur…
VocSegMRI: Multimodal Learning for Precise Vocal Tract Segmentation in Real-time MRI
Daiqi Liu, Johannes Enk, Maureen Stone +7
Accurate segmentation of articulatory structures in real-time MRI (rtMRI) remains challenging, as existing methods rely primarily on visual cues and overlook complementary informat…
Brightness-Invariant Tracking Estimation in Tagged MRI
Zhangxing Bian, Shuwen Wei, Xiao Liang +10
Magnetic resonance (MR) tagging is an imaging technique for noninvasively tracking tissue motion in vivo by creating a visible pattern of magnetization saturation (tags) that defor…
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 Audio Generation from dynamic MRI via a Knowledge Enhanced Conditional Variational Autoencoder
Yaxuan Li, Han Jiang, Yifei Ma +3
Dynamic Magnetic Resonance Imaging (MRI) of the vocal tract has become an increasingly adopted imaging modality for speech motor studies. Beyond image signals, systematic data loss…
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models
Shihua Qin, Ming Zhang, Juan Shan +3
Bone marrow lesions (BMLs) are critical indicators of knee osteoarthritis (OA). Since they often appear as small, irregular structures with indistinguishable edges in knee magnetic…
Label-Efficient 3D Brain Segmentation via Complementary 2D Diffusion Models with Orthogonal Views
Jihoon Cho, Suhyun Ahn, Beomju Kim +8
Deep learning-based segmentation techniques have shown remarkable performance in brain segmentation, yet their success hinges on the availability of extensive labeled training data…
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…
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…
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…
MomentaMorph: Unsupervised Spatial-Temporal Registration with Momenta, Shooting, and Correction
Zhangxing Bian, Shuwen Wei, Yihao Liu +6
Tagged magnetic resonance imaging (tMRI) has been employed for decades to measure the motion of tissue undergoing deformation. However, registration-based motion estimation from tM…
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…
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…
DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue
Zhangxing Bian, Fangxu Xing, Jinglun Yu +6
Tagged magnetic resonance imaging~(MRI) has been used for decades to observe and quantify the detailed motion of deforming tissue. However, this technique faces several challenges…
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…
Subtype-Aware Dynamic Unsupervised Domain Adaptation
Xiaofeng Liu, Fangxu Xing, Jia You +4
Unsupervised domain adaptation (UDA) has been successfully applied to transfer knowledge from a labeled source domain to target domains without their labels. Recently introduced tr…
Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives
Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing +4
Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performanc…
ACT: Semi-supervised Domain-adaptive Medical Image Segmentation with Asymmetric Co-training
Xiaofeng Liu, Fangxu Xing, Nadya Shusharina +4
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 targe…