9 citations · 23 across the 9 of their papers we have counts for
11 papers
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…
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…