25 citations · 128 across the 49 of their papers we have counts for
11 papers · 1 filter
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…