4 citations · 10 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…
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?
Weixun Luo, Xiaodan Xing, Guang Yang
Featured by a bottleneck structure, autoencoder (AE) and its variants have been largely applied in various medical image analysis tasks, such as segmentation, reconstruction and de…
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…