4 citations · 11 across the 11 of their papers we have counts for
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The Beauty or the Beast: Which Aspect of Synthetic Medical Images Deserves Our Focus?
Xiaodan Xing, Yang Nan, Federico Felder +2
Training medical AI algorithms requires large volumes of accurately labeled datasets, which are difficult to obtain in the real world. Synthetic images generated from deep generati…
You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images
Xiaodan Xing, Federico Felder, Yang Nan +3
Synthetic images generated from deep generative models have the potential to address data scarcity and data privacy issues. The selection of synthesis models is mostly based on ima…
Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations
Xiaodan Xing, Giorgos Papanastasiou, Simon Walsh +1
As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating correspond…
Hierarchical Perception Adversarial Learning Framework for Compressed Sensing MRI
Zhifan Gao, Yifeng Guo, Jiajing Zhang +2
The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI) because it leads to patient discomfort and motion artifacts. Although several MRI techni…