A visual-language foundation model for pathology image analysis using medical Twitter

The lack of annotated publicly available medical images is a major barrier for computational research and education innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter. Here we harness these crowd platforms to curate OpenPath, a large dataset of 208,414 pathology images paired with natural language descriptions. We demonstrate the value of this resource by developing pathology language-image pretraining (PLIP), a multimodal artificial intelligence with both image and text understanding, which is trained on OpenPath. PLIP achieves state-of-the-art performances for classifying new pathology images across four external datasets: for zero-shot classification, PLIP achieves F1 scores of 0.565-0.832 compared to F1 scores of 0.030-0.481 for previous contrastive language-image pretrained model. Training a simple supervised classifier on top of PLIP embeddings also achieves 2.5% improvement in F1 scores compared to using other supervised model embeddings. Moreover, PLIP enables users to retrieve similar cases by either image or natural language search, greatly facilitating knowledge sharing. Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing and education. Using extracted images and related labels from pathology-related tweets, a model is trained to associate tissue images and text and approaches state-of-the-art performance in clinically relevant tasks, such as tissue classification.

Automated summary

The lack of annotated publicly available medical images is a major barrier for computational research and education innovations. This study utilizes de-identified images and knowledge shared by clinicians on public forums to curate a large dataset called OpenPath, which consists of 208,414 pathology images paired with natural language descriptions. The researchers develop a multimodal artificial intelligence, PLIP, which is trained on OpenPath and achieves state-of-the-art performances for classifying pathology images. PLIP also enables users to retrieve similar cases by either image or natural language search, facilitating knowledge sharing.