Laboratory of Quantitative Imaging and Artificial Intelligence
Our research group uses artificial intelligence (AI) and computational methods to leverage the information in radiology images to enable biomedical discovery and to guide physicians in personalized care. Just as biology has been revolutionized by online genetic data, our goal is to advance radiology by making the content in images and medical texts computable and to electronically correlate images and texts with other clinical data such as pathology and molecular data. Our work develops and translates basic biomedical informatics methods to improve radiology practice and decision making in several areas to enable precision healthcare. A recent focus of the lab is in deep learning methods for automated image classification, lesion detetction, segmentation, and clinical prediction. We are developing novel methods to tackle recent challenges in AI related to limited quality labeled data, including weak learning, multi-task learning, and multi-modal models. We are also focusing on tackling the challenge of making clinical predictions on longitudinal image and text data and we have several important recent advances in predicting patient survival and disease progression.
We also develop tools to efficiently and thoroughly capture the semantic terms radiologists use to describe lesions; standardized terminologies to enable radiologists to describe lesions comprehensively and consistently; image processing methods to characterize lesions; content-based image retrieval with structured image information to enable radiologists to find similar images; methods to enable physicians to quantitatively and reproducibly assess tumor burden in images and to more effectively monitor treatment response in cancer treatment; natural language techniques to enable uniform indexing, searching, and retrieval of radiology information resources such as radiology reports; and decision support applications that relate radiology findings to diagnoses to improve diagnostic accuracy.
We collaborate with a variety of investigators at Stanford both in Radiology and Oncology as well as with investigators outside Stanford. We are engaged in scientific collaborations with the National Center for Biomedical Ontology, and we participate in a national working group that is developing imaging informatics infrastructure for the cancer Biomedical Informatics Grid program at National Cancer Institute. Our ultimate goal is to bridge the divide between radiological knowledge and practice--for all radiological knowledge and research data to be structured, accessed, and processed by computers so that we can create and deploy decision support applications in image workstations to improve radiologist clinical effectiveness.
The Rubin Lab is in the Department of Biomedical Data Science and the Department of Radiology in the Stanford University School of Medicine, and is a core faculty laboratory in the Biomedical Informatics Training Program at Stanford University. The Rubin Lab also serves as the Center for Imaging Informatics at Stanford University Hospital. The Rubin Lab is also affiliated with the Stanford Center for Biomedical Informatics Research (formally Stanford Medical Informatics or SMI).