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Recent accelerations in multi-modal applications have been made possible withthe plethora of image and text data available online. However, the scarcity ofanalogous data in the medical field, specifically in histopathology, has slowedcomparable progress. To enable similar representation learning forhistopathology, we turn to YouTube, an untapped resource of videos, offering$1,087$ hours of valuable educational histopathology videos from expertclinicians. From YouTube, we curate QUILT: a large-scale vision-languagedataset consisting of $802, 144$ image and text pairs. QUILT was automaticallycurated using a mixture of models, including large language models, handcraftedalgorithms, human knowledge databases, and automatic speech recognition. Incomparison, the most comprehensive datasets curated for histopathology amassonly around $200$K samples. We combine QUILT with datasets from other sources,including Twitter, research papers, and the internet in general, to create aneven larger dataset: QUILT-1M, with $1$M paired image-text samples, marking itas the largest vision-language histopathology dataset to date. We demonstratethe value of QUILT-1M by fine-tuning a pre-trained CLIP model. Our modeloutperforms state-of-the-art models on both zero-shot and linear probing tasksfor classifying new histopathology images across $13$ diverse patch-leveldatasets of $8$ different sub-pathologies and cross-modal retrieval tasks.

Quilt-1M: One Million Image-Text Pairs for Histopathology