StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929418735190016 |
|---|---|
| author | Jewsbury, Robert Wang, Ruoyu Bhalerao, Abhir Rajpoot, Nasir Vu, Quoc Dang |
| author_facet | Jewsbury, Robert Wang, Ruoyu Bhalerao, Abhir Rajpoot, Nasir Vu, Quoc Dang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09302 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images Jewsbury, Robert Wang, Ruoyu Bhalerao, Abhir Rajpoot, Nasir Vu, Quoc Dang Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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. |
| title | StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.09302 |