LIBS-160K

Radionuclide bone scan is one of the most important modalities for early diagnosis of malignant bone metastases. Despite many computer-assisted bone scanning diagnostic studies have been proposed to solve the problem of time-consuming and laborious manual diagnosis, the absence of publicly accessible data and benchmark has hindered research on foundational pre-training models in this field. To address this gap, we present LIBS-160K, a large-scale image-text paired bone scan dataset comprising 160,362 bone scan images from 6,586 patients. Each image is accompanied by corresponding diagnostic texts in both Chinese and English with image classification labels. Furthermore, the bone scan language–image pre-training (BoneSLIP) model is proposed, which leverages both vision and language to better comprehend bone scan images and text. BoneSLIP achieves good results on various sub-tasks such as bone metastasis prediction, differentiate anatomical regions and image-text retrieval. Our study proposes the first large-scale public dataset of bone scan image-text pairs and vision-language model, providing a foundation for the application of multimodal foundation models in the field of computer-aided bone scan analysis.