239 citations · 294 across the 11 of their papers we have counts for
12 papers
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Xin Yu, Qi Yang, Yinchi Zhou +12
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical…
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…
Outlier Guided Optimization of Abdominal Segmentation
Yuchen Xu, Olivia Tang, Yucheng Tang +9
Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image proc…
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…