output
20202024
most citedRip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids

15 citations

7 papers

cs.CV202415 cited

Rip-NeRF: Anti-aliasing Radiance Fields with Ripmap-Encoded Platonic Solids

Junchen Liu, Wenbo Hu, Zhuo Yang +6

Despite significant advancements in Neural Radiance Fields (NeRFs), the renderings may still suffer from aliasing and blurring artifacts, since it remains a fundamental challenge t…

stat.ME20216 cited

A Nonparametric Statistical Method for Two Crossing Survival Curves

Xinghui Huang, Jingjing Lyu, Yawen Hou +1

In comparative research on time-to-event data for two groups, when two survival curves cross each other, it may be difficult to use the log-rank test and hazard ratio (HR) to prope…

stat.AP202114 cited

Dynamic prediction and analysis based on restricted mean survival time in survival analysis with nonproportional hazards

Zijing Yang, Hongji Wu, Yawen Hou +2

In the process of clinical diagnosis and treatment, the restricted mean survival time (RMST), which reflects the life expectancy of patients up to a specified time, can be used as…

stat.AP2021

Combined tests based on restricted mean time lost for competing risks data

Jingjing Lyu, Yawen Hou, Zheng Chen

Competing risks data are common in medical studies, and the sub-distribution hazard (SDH) ratio is considered an appropriate measure. However, because the limitations of hazard its…

stat.ME202014 cited

The use of restricted mean time lost under competing risks data

Jingjing Lyu, Yawen Hou, Zheng Chen

Background: Under competing risks, the commonly used sub-distribution hazard ratio (SHR) is not easy to interpret clinically and is valid only under the proportional sub-distributi…

eess.IV20201 cited

Automatic Segmentation of Non-Tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks

Zhongqiang Liu

In brain tumor diagnosis and surgical planning, segmentation of tumor regions and accurate analysis of surrounding normal tissues are necessary for physicians. Pathological variabi…