25 citations · 65 across the 24 of their papers we have counts for
26 papers
UniMRSeg: Unified Modality-Relax Segmentation via Hierarchical Self-Supervised Compensation
Xiaoqi Zhao, Youwei Pang, Chenyang Yu +5
Multi-modal image segmentation faces real-world deployment challenges from incomplete/corrupted modalities degrading performance. While existing methods address training-inference…
Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging
Menghua Xia, Kuan-Yin Ko, Der-Shiun Wang +12
Positron emission tomography (PET) image denoising, along with lesion and organ segmentation, are critical steps in PET-aided diagnosis. However, existing methods typically treat t…
PET mapping of receptor occupancy using joint direct parametric reconstruction
Thibault Marin, Vasily Belov, Yanis Chemli +8
Receptor occupancy (RO) studies using PET neuroimaging play a critical role in the development of drugs targeting the central nervous system (CNS). The conventional approach to est…
Nuclear Medicine AI in Action: The Bethesda Report (AI Summit 2024)
Arman Rahmim, Tyler J. Bradshaw, Guido Davidzon +10
The 2nd SNMMI Artificial Intelligence (AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD, on February 29 - March 1, 2024. Bringing together various commu…
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…
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…
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…
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…
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…
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…
Self-semantic contour adaptation for cross modality brain tumor segmentation
Xiaofeng Liu, Fangxu Xing, Georges El Fakhri +1
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…
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…
Dual-cycle Constrained Bijective VAE-GAN For Tagged-to-Cine Magnetic Resonance Image Synthesis
Xiaofeng Liu, Fangxu Xing, Jerry L. Prince +4
Tagged magnetic resonance imaging (MRI) is a widely used imaging technique for measuring tissue deformation in moving organs. Due to tagged MRI's intrinsic low anatomical resolutio…
VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI
Xiaofeng Liu, Fangxu Xing, Chao Yang +5
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 da…
Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis
Xiaofeng Liu, Xiongchang Liu, Bo Hu +7
Recent advances in unsupervised domain adaptation (UDA) show that transferable prototypical learning presents a powerful means for class conditional alignment, which encourages the…
A Unified Conditional Disentanglement Framework for Multimodal Brain MR Image Translation
Xiaofeng Liu, Fangxu Xing, Georges El Fakhri +1
Multimodal MRI provides complementary and clinically relevant information to probe tissue condition and to characterize various diseases. However, it is often difficult to acquire…
A layered single-side readout DOI TOF-PET detector
L. Bläckberg, S. Sajedi, G. El Fakhri +1
We are exploring a scintillator-based detector with potential of high sensitivity, DOI capability and timing resolution in a single-side readout configuration. Our concept combines…