24 citations · 51 across the 8 of their papers we have counts for
6 papers · 1 filter
Characterizing Renal Structures with 3D Block Aggregate Transformers
Xin Yu, Yucheng Tang, Yinchi Zhou +10
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology. However, the development and evaluation of t…
RAP-Net: Coarse-to-Fine Multi-Organ Segmentation with Single Random Anatomical Prior
Ho Hin Lee, Yucheng Tang, Shunxing Bao +3
Performing coarse-to-fine abdominal multi-organ segmentation facilitates to extract high-resolution segmentation minimizing the lost of spatial contextual information. However, cur…
Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiologic…
Stochastic tissue window normalization of deep learning on computed tomography
Yuankai Huo, Yucheng Tang, Yunqiang Chen +8
Tissue window filtering has been widely used in deep learning for computed tomography (CT) image analyses to improve training performance (e.g., soft tissue windows for abdominal C…
Contrast Phase Classification with a Generative Adversarial Network
Yucheng Tang, Ho Hin Lee, Yuchen Xu +10
Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context…
Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision
Ho Hin Lee, Yucheng Tang, Olivia Tang +9
Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and e…