24 citations · 91 across the 19 of their papers we have counts for
20 papers
Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging
Shunxing Bao, Yucheng Tang, Ho Hin Lee +8
Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of 'seeing is believing…
Compound Figure Separation of Biomedical Images with Side Loss
Tianyuan Yao, Chang Qu, Quan Liu +11
Unsupervised learning algorithms (e.g., self-supervised learning, auto-encoder, contrastive learning) allow deep learning models to learn effective image representations from large…
Semantic-Aware Contrastive Learning for Multi-object Medical Image Segmentation
Ho Hin Lee, Yucheng Tang, Qi Yang +6
Medical image segmentation, or computing voxelwise semantic masks, is a fundamental yet challenging task to compute a voxel-level semantic mask. To increase the ability of encoder-…
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…
Development and Characterization of a Chest CT Atlas
Kaiwen Xu, Riqiang Gao, Mirza S. Khan +8
A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complic…
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…