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

• URL: http://arxiv.org/abs/2306.11207v3
• Date: Fri, 27 Oct 2023 23:56:33 GMT
• Title: Quilt-1M: One Million Image-Text Pairs for Histopathology
• Authors: Wisdom Oluchi Ikezogwo, Mehmet Saygin Seyfioglu, Fatemeh Ghezloo, Dylan Stefan Chan Geva, Fatwir Sheikh Mohammed, Pavan Kumar Anand, Ranjay Krishna, Linda Shapiro
• Abstract summary: We use YouTube to curate a vision-language dataset consisting of $802, 144$ image and text pairs. We combine QUILT with datasets from other sources, including Twitter, research papers, and the internet in general, to create QUILT-1M. Our model outperforms state-of-the-art models on both zero-shot and linear probing tasks for classifying new histopathology images.
• Score: 10.263853626151297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online. However, the scarcity of analogous data in the medical field, specifically in histopathology, has slowed comparable progress. To enable similar representation learning for histopathology, we turn to YouTube, an untapped resource of videos, offering $1,087$ hours of valuable educational histopathology videos from expert clinicians. From YouTube, we curate QUILT: a large-scale vision-language dataset consisting of $802, 144$ image and text pairs. QUILT was automatically curated using a mixture of models, including large language models, handcrafted algorithms, human knowledge databases, and automatic speech recognition. In comparison, the most comprehensive datasets curated for histopathology amass only around $200$K samples. We combine QUILT with datasets from other sources, including Twitter, research papers, and the internet in general, to create an even larger dataset: QUILT-1M, with $1$M paired image-text samples, marking it as the largest vision-language histopathology dataset to date. We demonstrate the value of QUILT-1M by fine-tuning a pre-trained CLIP model. Our model outperforms state-of-the-art models on both zero-shot and linear probing tasks for classifying new histopathology images across $13$ diverse patch-level datasets of $8$ different sub-pathologies and cross-modal retrieval tasks.

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