  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.
