activity
20172026
most citedDeep learning-based GTV contouring modeling inter- and intra- observer variability in sarcomas

25 citations · 128 across the 49 of their papers we have counts for

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Showing 2023Show all

10 papers · 1 filter

physics.med-ph2023

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…

cs.SD2023

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…

cs.CL2023★ 9 cited

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…

eess.IV2023

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…

cs.CV2023

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…

eess.IV2023

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…