26 citations · 63 across the 19 of their papers we have counts for
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Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield
Xiaodan Xing, Siofra Murdoch, Chunling Tang +7
Cell imaging assays utilizing fluorescence stains are essential for observing sub-cellular organelles and their responses to perturbations. Immunofluorescent staining process is ro…
Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba
Jiahao Huang, Liutao Yang, Fanwen Wang +8
Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant para…
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…
Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation
Zhaohu Xing, Liang Wan, Huazhu Fu +2
In recent years, Denoising Diffusion Models have demonstrated remarkable success in generating semantically valuable pixel-wise representations for image generative modeling. In th…
A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning
Jin Zhu, Guang Yang, Pietro Lio
Super-resolution plays an essential role in medical imaging because it provides an alternative way to achieve high spatial resolutions and image quality with no extra acquisition c…