Linking spatial biology and clinical histology via Haiku

Haiku is a tri-modal contrastive learning model trained on multiplexed immunofluorescence (mIF), integrating molecular, morphological, and clinical data from over 1,600 patients. It enables three-way cross-modal retrieval, improves downstream classification and clinical prediction tasks, and supports zero-shot biomarker inference, outperforming competing approaches.