| Title: |
Multistain Pretraining for Slide Representation Learning in Pathology |
| Authors: |
Jaume, Guillaume, Vaidya, Anurag, Zhang, Andrew, Song, Andrew H., Chen, Richard J., Sahai, Sharifa, Mo, Dandan, Madrigal, Emilio, Le, Long Phi, Mahmood, Faisal |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
Developing self-supervised learning (SSL) models that can learn universal and transferable representations of H&E gigapixel whole-slide images (WSIs) is becoming increasingly valuable in computational pathology. These models hold the potential to advance critical tasks such as few-shot classification, slide retrieval, and patient stratification. Existing approaches for slide representation learning extend the principles of SSL from small images (e.g., 224 x 224 patches) to entire slides, usually by aligning two different augmentations (or views) of the slide. Yet the resulting representation remains constrained by the limited clinical and biological diversity of the views. Instead, we postulate that slides stained with multiple markers, such as immunohistochemistry, can be used as different views to form a rich task-agnostic training signal. To this end, we introduce Madeleine, a multimodal pretraining strategy for slide representation learning. Madeleine is trained with a dual global-local cross-stain alignment objective on large cohorts of breast cancer samples (N=4,211 WSIs across five stains) and kidney transplant samples (N=12,070 WSIs across four stains). We demonstrate the quality of slide representations learned by Madeleine on various downstream evaluations, ranging from morphological and molecular classification to prognostic prediction, comprising 21 tasks using 7,299 WSIs from multiple medical centers. Code is available at https://github.com/mahmoodlab/MADELEINE.
Comment: ECCV'24 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2408.02859 |
| Accession Number: |
edsarx.2408.02859 |
| Database: |
arXiv |
