StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images

• URL: http://arxiv.org/abs/2403.09302v2
• Date: Fri, 12 Jul 2024 16:27:06 GMT
• Title: StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
• Authors: Robert Jewsbury, Ruoyu Wang, Abhir Bhalerao, Nasir Rajpoot, Quoc Dang Vu,
• Abstract summary: 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. We propose a new approach, StainFuser, which treats this problem as a style transfer task using a novel Conditional Latent Diffusion architecture.
• Score: 5.382682403111961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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