StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
<|reference_start|>StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images: Stain normalization algorithms aim to transform the color and intensity characteristics of a source multi-gigapixel histology image to match those of a target image, mitigating inconsistencies in the appearance of stains used to highlight cellular components in the images. We propose a new approach, StainFuser, which treats this problem as a style transfer task using a novel Conditional Latent Diffusion architecture, eliminating the need for handcrafted color components. With this method, we curate SPI-2M the largest stain normalization dataset to date of over 2 million histology images with neural style transfer for high-quality transformations. Trained on this data, StainFuser outperforms current state-of-the-art deep learning and handcrafted methods in terms of the quality of normalized images and in terms of downstream model performance on the CoNIC dataset.<|reference_end|>
@article{jewsbury2024stainfuser:,
    title={StainFuser: Controlling Diffusion for Faster Neural Style Transfer in
  Multi-Gigapixel Histology Images},
    author={Robert Jewsbury, Ruoyu Wang, Abhir Bhalerao, Nasir Rajpoot, Quoc Dang
  Vu},
    journal={arXiv preprint arXiv:2403.09302},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09302},
    primaryClass={eess.IV cs.CV cs.LG}
}