19 citations · 29 across the 6 of their papers we have counts for
6 papers
HE-NAC: High-Efficiency Neural Audio Coding via Hierarchical and Residual Multi-Band Quantization
Darius Pétermann, Wootaek Lim, Seungkwon Beack +1
High-fidelity audio coding requires faithful reconstruction of perceptually important high-frequency components, which are often lost due to the aggressive temporal decimation and…
Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET
Ziping Liu, Joyce C. Mhlanga, Barry A. Siegel +1
Artificial intelligence (AI)-based methods are showing substantial promise in segmenting oncologic positron emission tomography (PET) images. For clinical translation of these meth…
No-gold-standard evaluation of quantitative imaging methods in the presence of correlated noise
Ziping Liu, Zekun Li, Joyce C. Mhlanga +2
Objective evaluation of quantitative imaging (QI) methods with patient data is highly desirable, but is hindered by the lack or unreliability of an available gold standard. To addr…
Observer study-based evaluation of a stochastic and physics-based method to generate oncological PET images
Ziping Liu, Richard Laforest, Joyce Mhlanga +5
Objective evaluation of new and improved methods for PET imaging requires access to images with ground truth, as can be obtained through simulation studies. However, for these stud…
A no-gold-standard technique to objectively evaluate quantitative imaging methods using patient data: Theory
Jinxin Liu, Ziping Liu, Joyce Mhlanga +2
Objective evaluation of quantitative imaging (QI) methods using measurements directly obtained from patient images is highly desirable but hindered by the non-availability of gold…
A Bayesian approach to tissue-fraction estimation for oncological PET segmentation
Ziping Liu, Joyce C. Mhlanga, Richard Laforest +3
Tumor segmentation in oncological PET is challenging, a major reason being the partial-volume effects that arise due to low system resolution and finite voxel size. The latter resu…